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Two separate files are concatenated. If TeXing, remember to remove _both_
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Newsgroups: sci.math
From: ceblair@ux1.cso.uiuc.edu (Charles Blair)
Subject: CRYPTOGRAPHY NOTES (part 1 of 2)
Date: Tue, 8 Sep 1992 13:26:23 GMT

%  These are a set of notes for a course I taught in Fall 1991 on 
% public-key cryptography and related topics: 
%
% 1. Encryption Systems
% 2. Elementary Number Theory 3. RSA and Rabin systems
% 4. Knapsack systems (I don't discuss breaking them.  Sorry!)
% 5. Introduction to NP-Completeness 6. Rabin's primality test
% 7. Probabilistic Encryption (Goldwasser-Micali)
% 8. Pseudo-random number generators (Blum-Blum-Shub)
% 9. Further results on pseudo-random numbers
%
% [There have been some minor corrections, and the short section 9
%  is new since the original posting.]
% 
%   This is a LaTeX file in two parts (45 pages when printed).  Concat-
% enate the two parts before running LaTeX.  Advanced readers who only
% want sections 7-9 can delete from the line \section{Encryption Systems}
% to the end of this file before concatenating.
%
%   I have included a copyright announcement ``just in case.''  However,
% I specifically authorize use of this material for teaching classes,
% inclusion in ftp sites, and similar non-profit activities.  I would,
% of course, appreciate appropriate attribution.
%
%   I do not claim to have proven any new results and tried to give
% appropriate credits.  However, I hope that my exposition may be easier
% to understand than other versions.
%
%   Suggestions and (especially) corrections are welcome.
%
%    Charles Blair (ceblair@ux1.cso.uiuc.edu)
%
\documentstyle[12pt]{article}\begin{document}
\title{Notes on Cryptography }\author{Charles Blair\\
Business Administration Dept.\\University of Illinois\\
ceblair@ux1.cso.uiuc.edu}
\date{\copyright1991 by the author}\maketitle\tableofcontents
\newtheorem{Th}{Theorem}
\newtheorem{Le}[Th]{Lemma}\newtheorem{Co}[Th]{Corollary}
\newcommand{\pq}{\par\medskip}
\newcommand{\co}[3]{#1\equiv#2\hbox{ mod }#3}
\newcommand{\ro}{\hbox{ or }}
\newcommand{\zo}{Z^1_n}\newcommand{\ps}{p_{PS}}
\newcommand{\T}{{\bf T}} \newcommand{\F}{{\bf F}}
\newenvironment{lst}
{\begin{center}\begin{tabular}{l}}{\end{tabular}\end{center}}
\section{Encryption Systems}
An encryption system is a procedure which takes the original message
({\it plaintext\/}) and a small piece of information arranged in advance
between sender and receiver (the {\it key\/}) and creates an encoded
version of the message (the {\it ciphertext\/}).
\pq When we are considering the quality of an encryption system, we assume
the person trying to decode the message knows what the general pro\-cedure
is and is looking at the ciphertext--- the only thing he does not have
is the key.  We also assume the person sending messages does not spend
time trying to contrive a difficult-to-read message by using unusual
words or letter frequencies--- the sender is counting on the system to
provide all the needed security.
\pq Usually one assumes the person trying to break the code is only
working with the ciphertext.  However, there are situations in which
both plaintext and ciphertext of a previously encoded message are 
available.  For example, I often keep encrypted versions of examinations
on a mainframe computer, only decoding them just before having them
printed, and deleting the plaintext file immediately afterward.  If a
student was able to look at my files, he could keep a copy of the
encoded test and compare this with the test he took.  As we will see,
this may be very useful in decoding future tests.
\pq[One countermeasure against this type of {\it known-plaintext attack\/}
is to continually change keys, assuming an encryption using one key is
not helpful on a different key.  It can become difficult to keep track
of the different keys in use, especially if they are long.]
\pq A more demanding standard is that a code may be safe against a
{\it chosen-plaintext attack}.  We are imagining that the encryption is
done by a machine, and that unauthorized persons may have access to
the machine (although we assume they are only using it in the normal
way, not allowed to take it apart).
\subsection*{Example 1: Simple substitution}
This is the simple letter-for-letter method found in Poe's ``The Gold
Bug'' and many other stories.  The key is a rearrangement of the
26 letters:\begin{lst}\tt ABCDEFGHIJKLMNOPQRSTUVWXYZ\\ \tt
actqgwrzdevfbhinsymujxplok\end{lst}
  Using this key, the plaintext:\begin{lst}\tt \label{pl}
  THE SECURITY OF THE RSA ENCODING SCHEME RELIES ON THE\\
\tt FACT THAT NOBODY HAS BEEN ABLE TO DISCOVER HOW TO TAKE \\ \tt
CUBE ROOTS MOD N WITHOUT KNOWING NS FACTORS\end{lst}
becomes the ciphertext:\begin{lst}\tt
  UZG MGTJYDUO IW UZG YMA GHTIQDHR MTZGBG YGFDGM IH UZG\\ \tt
WATU UZAU HICIQO ZAM CGGH ACFG UI QDMTIXGY ZIP UI UAVG \\ \tt
TJCG YIIUM BIQ H PDUZIJU VHIPDHR HM WATUIYM\end{lst}
The messages can be made harder to decode (but also harder to read!)
by leaving out the spaces between words.
\pq Most messages can be decoded by looking for frequently occuring
pairs of letters ({\tt TH} and {\tt HE} are by far the most common),
using these to identify a few letters to begin, and filling in the
remaining letters one at a time (``The Gold Bug'' gives a good
description, as do many books).
\pq In a known-plaintext situation, the whole code is obtained 
almost immediately.  However, in our example, the letters {\tt J},
{\tt P}, and others do not occur in the plaintext, so we could not
tell how they are encoded.  If we were allowed a chosen plaintext,
we would  use all the letters to get the entire key.
\subsection*{Example 2: The Vigen\`ere cipher and one-time pads}
This cipher works by replacing each letter by another letter a 
specified number of positions further in the alphabet.  For example
{\tt J} is 5 positions further than {\tt E}.  {\tt D} is 5 positions
after {\tt Y}.  ({\tt Y,Z,A,B,C,D})  The key is a sequence of 
shift amounts.  If the sequence is of length~10, the 1st, 11th, 21st,
\dots letters of the plaintext are processed using the first member
of the key.  The second member of the key processes plaintext letters
2, 12, 22, \dots and so forth.  If we omit spaces from the plaintext
on page~\pageref{pl}\  and use the key-sequence:$$3\ 1\ 7\ 23\ 10\ 5\ 19\ 14\ 
19\ 24$$we obtain \begin{lst} \tt
WILPOHNFBR BPMQRJKGTC QDVASSZGVF\\ \tt
HNLOOQBSLM QUOBPFVHMF DUULLTWMAY VCLBXFUZXR\\ \tt
REPPMTOSKF RXALDFDSVS EFYLYYLAHB QXPQRTNHDL\\ \tt
RXPKQSLTTA WPYP\end{lst}
(We have divided the ciphertext into groups of ten letters for
convenience.  The division into lines is arbitrary.)
\pq This type of cipher was considered very secure at one time
($\sim1600$), but is not really difficult.  Suppose we guess that
the first letter is {\tt T}.  This implies the eleventh letter 
is {\tt Y}, the 21st letter is {\tt N}, etc.  Now look at the
two-letter combinations that occur from different possiblilities for the
second letter:\begin{lst}{\tt
TI YP ND EN NU AU SC OE OX BF NX OX TP}\quad(no shift of 2nd letter)\\
\tt TJ YQ NE EO NV AV SD OF OY BG NY OY TQ\\ \tt
TK YR NF EP NW AW SE OG OZ BH NZ OZ TR\\ \tt
TL YS NG EQ NX AX SF OH OA BI NA OA TS\\
\quad(skipping over some in the middle)\\ \tt
TF YM NA EK NR AR SZ OB OU BE NU OU TM\\ \tt
TG YN NB EL NS AS SA OC OV BD NV OV TN\\ \tt
TH YO NC EM NT AT SB OD OW BE NW OW TO\end{lst}
The last line is the ``right answer.''  Although it shows several
bad combinations ({\tt NC NT SB NW}), mostly caused by the last
letter of one word being adjacent to the first letter of the next
word, it looks better than the other possible rows.  Once the
second letter has been identified, the same approach can be used
to get the third letter. This approach is easily automated using
a table of digrams.\pq It is necessary to know the first letter
and the length of the key-sequence. If we assume the length is
not too large, a program can just try all possibilities, eventually
choosing the plaintext which looks best.\footnote{Mike Mendelson,
a student in this course in 1989, wrote a program to implement this 
algorithm.  Another method  would choose the shift
amount for each member of the cycle which gives the best letter frequency.}
\subsubsection*{One-time pads}
\pq A long key-sequence makes this approach more difficult, since
we have fewer rows.  The extreme case is that in which the key-%
sequence is as long as the plaintext itself.  This leads to a
theoretically {\it unbreakable\/} cipher.  For any possible plain%
text, there is a key for which the given ciphertext comes from
that plaintext.\pq This type of cipher has reportedly been used
by spies, who were furnished with notebooks containing page after
page of randomly generated key-sequence.  Notice that it is essential
that each key-sequence be used only once (hence the name of the
system).  Otherwise the approach for Vigen\`ere systems described
above could be tried, since we would have at least two rows to
work with.\pq One-time pads seem practical in situations where one
agent is communicating with a central command.  They become less
attractive if several agents may
need to communicate with each other.  The one-time feature is lost
if X and Y inadvertently use the same page to talk as W and Z are
using.  Also capture of X's equipment makes it possible to overhear
a conversation between Y and Z.
\subsection*{Example 3: A Transposition System}
In this system, we will assume every line of the message is 63
characters long.  The key is a permutation of the numbers from
1 to 63, and each line of the plaintext is rearranged using the
permutation to produce the corresponding ciphertext.  For example
if the key is $$1\ 11\ 21\dots61\ 8\ 18\dots54$$(we would really want
to use a more complicated permutation) and we use the same
plaintext as in the previous two examples, we obtain:\begin{lst}\tt
TTRNRT UHOMO SFECE HYSGEH REDEN E NHS E A LE I I\ \ \ CTCE\ \ \ O SI\\ \tt
FN\ \ ET AHBCT\ \ DNDO AOBTRA TALOO TY IW CBEO K%
\ \ SEV\ \ H AS\ \ TOE HE\\ \tt
C HNO\ \ OWOA\ \ \ \ \ S\ \ UMOGR\ \ TIWC\ \ RNK%
\ \ \ BOU S\ \ STIT\ \ O NF\ \ EDTN\end{lst}
We are using the version of the plaintext including blanks.  The
second line of the plaintext has 55 characters, so we add 8 blanks
on the end.
\pq One method of decoding looks at a column of the ciphertext and
asks what other column could immediately follow it.  For example,
it is possible that the column following {\tt OBO} (the tenth
ciphertext column) is {\tt UAO} (the 8th), but the column {\tt TFC} would
yield the improbable two-letter combination {\tt BF}.
\pq As always, a longer message is easier to decode.  Unlike simple
substitution, it seems that blanks make the decoding process more 
difficult.
\pq What about a known-plaintext attack? Since there is only one 
Y in the first line of the plaintext, we can tell that column~12
of the plaintext is column~21 of the ciphertext, but there are
other things we can't tell.  In this example, there are 8 columns
of three blanks at the end of the plaintext, and we can't be sure
which of these corresponds to which of the all-blank ciphertext
columns.  (it doesn't matter for this message, but we would like to
know the entire key to deal with longer plaintexts in the future)
A carefully chosen plaintext can give us the entire key at once.
\section{Introduction to Number Theory\label{numth}}\subsection{Congruences}
The congruence $\co abn$ (``$a$ is congruent to $b$ mod $n$'') says that,
when divided by $n$, $a$ and $b$ have the same remainder.
$$\co{100}{34}{11}\qquad\co{-6}{10}{8}$$
In the second congruence, we are using $-6=8(-1)+2$. We always have
$\co abn$ for some $0\le b\le n-1$, and we are usually concerned with
that $b$.  If $\co abn$ and $c\equiv d$, we can add or multiply
$$\co {a+c}{b+d}n\qquad \co{ac}{bd}n$$
Division does not always work: $\co6{18}{12}$ but $3\not\equiv9$.
\subsection{The Greatest Common Divisor}
For $a$ and $b$, the number $(a,b)$ is the largest number which
divides $a$ and $b$ evenly. $$(56,98)=14\qquad(76,190)=38$$\begin{Th}
\label{T1}For any $a,b$ there are integers $x,y$ with $ax+by=(a,b)$\end{Th}
Proof: The equation can be solved by making a sequence of simplifying
substitutions:\begin{eqnarray*}30x+69y&=&3\\30x'+9y&=&3\quad[x'=x+2y]\\
3x'+9y'&=&3\quad[y'=y+3x']\\3x''+0y'&=&3\quad[x''=x'+3y']\end{eqnarray*}
It is easy to see that $x''=1$, $y'=0$ is a solution to the final
equation and we get a solution to the original equation by working
backwards:$$x'=x''-3y'=1\quad y=y'-3x'=-3\quad x=x'-2y=7$$
\pq We could also solve an equation like $30x+69y=15$ by multiplying
our solution: $y=-15$, $x=35$.  It should be clear that the equation
will have no solution in integers if 15 is replaced by something
that is not a multiple of $(30,69)=3$.\pq All other integer solutions of
$30x+69y=15$ may be obtained by changing the first solution:
$$y=-15+\frac{30}3t\quad x=35-\frac{69}3t\quad\hbox{for $t$ integer}$$
\pq If we do the process illustrated on the previous page for any
equation $ax+by=(a,b)$, we eventually get one of the coefficients
as zero and the other as $(a,b)$.  [In fact, this process is usually
presented as ``Euclid's algorithm for finding the greatest common
divisor.'']
\pq It is important that this process is feasible [on a computer]
even if $a$ and $b$ are several hundred digits long.  It is easy
to show that the larger of the two coefficients decreases by at 
least $1/2$ every two equations, hence that in twenty equations
the larger coefficient has decreased by $2^{-10}<10^{-3}$, so
a 600-digit number would not require more than 4000 equations.
[this analysis can be improved]
\pq We pointed out earlier that division does not work with congruences.
An important application of Theorem~\ref{T1} is that it does work for prime
numbers.\begin{Co} If p is a prime number, $\co {ar}{as}p$ and 
$a\not\equiv 0$, then $r\equiv s$.\end{Co}
Proof: Since $p$ is a prime,
$(a,p)=1$, so Theorem~\ref{T1} says there are integer $x,y$ with $ax+py=1$.
Hence $$\co {ax}1p\quad\hbox{and}\quad r\equiv(1)r\equiv axr\equiv xar
\equiv\co {xas}sp$$
\begin{Co}If p is a prime number and $a\not\equiv0$~mod~$p$, then
for any $b$, there is $y$ with $\co{ay}bp$.\end{Co}
Proof: We showed in the preceding proof that there is $x$ with
$\co {ax}1p$.  Let $y=bx$.
\begin{Co}[The ``Chinese Remainder Theorem''] If $(p,q)=1$, then
for any $a,b$, there is an $n$ with $$\co nap\quad
\hbox{and}\quad\co nbq$$\end{Co}
Proof: Theorem~\ref{T1} implies there are integers $x,y$ such that
$$px+a=qy+b\quad\hbox{so let }n=px+a$$
\subsection{Powers modulo a prime}The sequence $$a\quad a^2\quad
a^3\dots\quad\hbox{mod }p$$ has many applications in cryptography.
Before looking at theoretical properties, the example below (done
using a pocket calculator) should make clear that it is practical
to compute these numbers, even when many digits are involved.
\pq Suppose we want to compute $432^{678}$~mod~987.  The basic trick
is to start with a number and keep squaring:
$$432^2=186624\equiv81\quad432^4\equiv81^2\equiv639\quad432^8\equiv
639^2\equiv690\dots432^{512}\equiv858$$
Since $678=512+128+32+4+2$, $$432^{678}\equiv(81)(639)\dots(858)
\equiv204\qquad\hbox{(I hope!)}$$Calculations with exponents 
involve not-too-many multiplications.  If the numbers have several
hundred digits, however, it is necessary to design special sub%
routines to do the multiplications (see Knuth, volume~2).
\pq Let us look at the sequence of powers of 2 mod 11:
$$2\ 4\ 8\ 5\ 10\ 9\ 7\ 3\ 6\ 1$$Each number from 1 to 10 appears
in the sequence.\begin{Th}\label{proot}Let $p$ be a prime.  There
is an $a$ such that for every $1\le b\le p-1$, there is $1\le x\le
p-1$ such that $\co{a^x}bp$.\end{Th}
It is not always the case that $a=2$.  The powers of 2 mod~7 are
2,~4,~1 after which the sequence repeats and we never get 3, 5, or 6.
\pq The proof of Theorem~\ref{proot} requires several steps, so we
will give it later.  For now, we want to look at some of its consequences.
\begin{Co}Let $a$ be as in Theorem~\ref{proot}. Then $\co{a^{p-1}}1p$.
\label{PF}\end{Co}Proof: We know that $a^d\equiv1$ for some $1\le d\le p-1$.  If
$d<p-1$, the sequence of powers of $a$ would start repeating before
we got all the numbers: $a^{d+1}\equiv a$, $a^{d+2}\equiv a^2$, etc.
\begin{Co}\label{Fe}For any $b\not\equiv0$,
 $\co{b^{p-1}}1p$.\end{Co}Proof: Let $a$ be
as in Theorem~\ref{proot}. Using Corollary~\ref{PF} $$b^{p-1}
\equiv a^{x(p-1)}\equiv\left(a^{p-1}\right)^x\equiv1$$
\begin{Co}If $\co xy{(p-1)}$, then $\co {b^x}{b^y}p$\end{Co}
Proof: For some integer $r$, $y=r(p-1)+x$ and by Corollary~\ref{Fe}
$$b^y\equiv\co{\left(b^{p-1}\right)^rb^x}{b^x}p$$
\begin{Le}\label{di}Let $b\not\equiv0$, $d$ the smallest
positive number such that $b^d\equiv1$.  Then for any $e>0$ with $b^e
\equiv 1$  $d$ divides $e$ evenly. In particular, by Corollary~\ref{Fe},
$d$ divides $p-1$ evenly.\end{Le}Proof: If $d$ does not divide $e$, then
$e=dr+s$ for some $0<s<d$, but $$b^s\equiv b^{e}\left(b^d\right)%
^{-r}\equiv1$$ would contradict the definition of $d$.
\subsection{Primitive roots}
Theorem~\ref{proot} showed that if $p$ is a prime, there is an $a$ such that
the equation $$\co{a^x}bp$$has a solution for any $b\not\equiv0$.
Such an $a$ is called a {\it primitive root\/} of $p$, and $x$
is called the {\it discrete logarithm\/} of $b$.\pq We showed in
the beginning of section~\ref{numth} that it is easy to obtain $b$ given
$a$ and $x$.  Finding $x$ given $a$ and $b$ is much harder.  Many modern
encryption systems are based on the fact that no efficient way of
computing discrete logarithms is known.
\pq No efficient method for always finding primitive roots is known.
However, it is often possible to find one in special cases.
We will use $p=1223$ as an example.  $p-1=2\cdot13\cdot47$. By Lemma~%
\ref{di}, if $a$ is not a primitive root, then we will either have
$a^{26}$, $a^{94}$, or $\co{a^{611}}1{1223}$.  $a=2$ and 3 fail, but
$a=5$ satisfies all three conditions, so it is a primitive root.  (we
could tell that $a=4$ would not be a primitive root without testing.  Why?)
\pq It is easy to show that, if $a$ is a primitive root, $a^x$ is a
primitive root if and only if $(x,p-1)=1$.  In this example, this means
the number of primitive roots is$$1222\left(\frac12\right)\left(\frac
{12}{13}\right)\left(\frac{46}{47}\right)=552$$Thus, if we had just chosen
$a$ at random, the probability that it would be a primitive root is
$\approx.45$.  Choosing $a$ at random and testing until we found a
primitive root would not be expected to take too long.
\pq This is an example of a {\it probabilistic algorithm}.  It is possible
for it to take a long time, but the amount of time needed on average is
reasonably small.  We will see many other probabilistic algorithms later.
\subsubsection*{Proof of Theorem \ref{proot}}
Let $a\not\equiv0$, $d$ be the smallest positive number for which $a^d
\equiv1$ (there must be such a $d$ since $a^K\equiv a^L$ implies $a^{K-L}
\equiv1$).  If $d=p-1$, $a$ is a primitive root.  If $d<p-1$, we will
find $a',d'$ with $d'>d$.  If $d'<p-1$ the process is repeated until
we eventually obtain a primitive root.\begin{Le}There are at most $d$
solutions to a congruence involving a polynomial of degree $d$:
$$\co{x^d+\alpha_1x^{d-1}+\dots\alpha_d}0p$$In particular, there are at
most $d$ $x$ with $x^d\equiv1$.\label{B}
\end{Le}{\bf Proof:} This can be proved in
the same way as the corresponding theorem in ordinary algebra: if $x=\beta$
is a solution, the polynomial can be written as $(x-\beta)$ times a poly%
nomial of degree $d-1$, which by induction has $\le d-1$ solutions.
\pq We return to the proof of Theorem~\ref{proot}. The sequence 
$$a\quad a^2\quad a^3\dots \co {a^d}1p$$consists of $d$ different solutions
of $x^d\equiv1$.  If $d<p-1$, let $b$ be any non-member of the sequence,
with $e$ the smallest positive number with $b^e\equiv1$. 
If $e>d$, we may take $a'=b$, so we will assume $e\le d$ from now on.  
By Lemma~\ref{B}, $b^d\not\equiv1$, which implies $e$ does not divide
$d$ and $e/(d,e)>1$. 
$$\hbox{Let\quad}a'=a^{(d,e)}b\qquad c=\frac 
d{(d,e)}$$To complete the proof, we will show that if $a'^x\equiv1$,
then $x$ is divisible by $ce=de/(d,e)>d$.\pq
Since $(c,e)=1$, Theorem~\ref{T1} implies there are $K,L$
with $cK+eL=1$. If $a'^x\equiv1$, then $a'^{cx}\equiv b^{cx}\equiv1$.
By Lemma~\ref{di}, $cx=eM$ for some integer $M$ and
$$x=(cK+eL)x=e(KM+Lx)$$so $x=ex'$ for some integer $x'$.  Together with
$a'^x\equiv1$ and Lemma~\ref{di}, this implies for some integer $N$
$$(d,e)ex'=dN\Rightarrow ex'=cN\Rightarrow (cK+eL)x'=c(Kx'+LN)$$so
$x'$ is divisible by $c$.
\section{Encryption techniques based on powers and congruences}
\subsection{The Diffie-Hellman key exchange procedure}
A and B are communicating.  C hears everything A and B say.
A and B want to agree on a number, without C knowing what the 
number is. It may be, for example, that A~and~B plan to use the
number as the key for future encoded messages.
The procedure (also often called a {\it protocol\/}):
\pq A and B agree on a (large) prime $p$ and a primitive root $a$.
These numbers are also known to C.  A secretly chooses a (large)
number $X_1$, B secretly chooses $X_2$.  $a^{X_1}$ and $a^{X_2}$
 mod~$p$ are publicly announced (hence known to C).  The secret number
will be $S=a^{X_1X_2}$~mod~$p$.
$$\hbox{A calulates }S\equiv\left(a^{X_2}\right)^{X_1}\qquad
\hbox{B calculates }S\equiv\left(a^{X_1}\right)^{X_2}$$
A possible drawback to this system is that neither A nor B controls
what $S$ is.  If $S$ is not a satisfactory number, they may have
to repeat the protocol.
\pq Diffie and Hellman suggest the procedure can also be used in
a situation in which $n$ people must find, for each pair of people,
an agreed-upon number.  For $1\le i,j\le n$ the number is $a^{X_iX_j}$.
\subsection{The Rivest-Shamir-Adleman public key system}
A sets up a system so that anyone can send him an encoded
message, but only A will be able to decode it.  The message is represented
as a number $M$.  The encoding is done by a publicly known function $f(M)$,
with A the only person who knows how to compute $f^{-1}$.
A chooses two large primes $p$, $q$ which he keeps secret.  He announces
$n=pq$ and another number $d$, with $(d,p-1)=(d,q-1)=1$ (one way to
do this is to choose $d$ a prime larger than $p/2$ and $q/2$.)
The encoding is $$f(M)\equiv M^d\hbox{
mod n}$$where $M$ and $f(M)$ are both $\le n-1$.
We have seen $f$ can be computed in a realistic amount of time
even if $M$, $d$, $n$ are many digits long.
\pq A computes $M$ from $M^d$ using his knowledge of $p$, $q$. By
 Corollary 8, $$\hbox{If }\co {de}1{(p-1)}\hbox{ then }\co{\left(M^d\right)^e}
1p$$Similarly $\co{\left(M^d\right)^e}Mq$ if $\co {de}1{(q-1)}$.
$e$ satisfies these two conditions if $\co{ed}1{(p-1)(q-1)}$.  Theorem~1
says we can let $e=x$, where $x$ is a solution of $$dx+(p-1)(q-1)y=1$$
Since $\left(M^d\right)^e-M$ is divisible by $p$ and by $q$, it is
divisble by $pq$, hence we can recover $M$ from $M^d$ by taking to
the $e$-th power mod $pq$.
\pq It is crucial to the security of this system that knowledge of
$n$ does not allow an eavesdropper to calculate $p$ and $q$. The
crude approach of dividing $n$ by all numbers up to $\sqrt n$ would
take $\sim10^{50}$ steps for a 100-digit $n$. However, many famous
mathematicians have been unable to devise significantly better
factoring algorithms, and this problem has been studied for at
least 100~years.
\pq One practical difficulty in using this system is the need to
do calculations with many-digit numbers, especially to find primes.
Another difficulty is that the inventors of this system have patented
it.  Amateur programmers who have posted implementations on electronic
bulletin boards have received letters from ``RSA Security, Inc''
warning of possible patent infringement.
\subsection{A public key system as hard as factoring\label{Ra}}
It is possible in theory that there is some way of computing $f^{-1}$
for the system in the previous section that does not involve deter%
mining $p$ and $q$. In the original RSA paper, the authors say
\begin{quotation}It may be possible to prove that any general method
of breaking our scheme yields an efficient factoring algorithm.  This
would establish that any way of breaking our scheme must be as diff%
icult as factoring. We have not been able to prove this conjecture,
however.\end{quotation}
To see the difficulties involved in trying to prove such a thing,
suppose that one could show that knowledge of a ciphertext $f(M)$
and a plaintext $M$ enabled one to find $p$ and $q$.  Then one 
could factor $n$ as follows:\begin{enumerate}
\item Choose any $M$.\item Compute $f(M)$.  [Remember, we are assuming
$f$ is publicly available.  Furthermore, $f(M)$ can't be too hard
to compute, or the code would be impractical.]\item Use the assumed
method to obtain $p$, $q$.\end{enumerate}
In words, we are unable to distinguish between the situation
in which $f(M)$ is obtained from $M$ (easy) and the (presumably difficult)
situation in which $M$ is obtained from $f(M)$.
\pq Rabin has suggested an alternative to the RSA system in which there
is a direct connection to factoring.  As in RSA, $n=pq$ is announced
publicly, with primes $p$, $q$ kept secret.  For technical reasons, we
assume $\co {p,q}34$.
The encoding function is  $$\co {f(M)}{M^2}n$$  
 The way we avoid the difficulty described above is that there are 
{\it four\/} numbers $M_1,M_2,M_3,M_4$ with $f(M_i)\equiv f(M)$.  The
key facts are:\begin{enumerate}\item If $p,q$ are known, it is easy
to compute all the $M_i$ given $f(M)$.\item If we are given $n$, $f(M)$,
and all the $M_i$, we can calculate $p$, $q$.
\end{enumerate}We are {\it not\/} able to obtain $p$, $q$ from just one
of the $M_i$, so the approach based on $M$ and $f(M)$ described above
won't work.  One drawback of this system is that, even with knowledge of
$p$ and $q$, one can only say the number sent is one of the four $M_i$,
without being able to identify which one.  In practice, this is not
serious, since it is very unlikely that more than one of the $M_i$ would
correspond to a feasible message.
\pq{\bf proof of 1:} Since $\co p34$, there is an integer $k$ with $4k=p+1$.
If $\co x{\left(f(M)\right)^k}p$, then using Corollary~8:
$$x^2\equiv \left(\left(M^2\right)^k\right)^2\equiv \co {M^{4k}}{M^2}p$$
Similarly if $\co y{\left(f(M)\right)^L}q$ [$4L=q+1$], then $\co {y^2}{M^2}q$.
The $M_i$ are given from Corollary~4 by$$\begin{array}{llll}
\co {M_1}xp&\co{M_2}xp&\co{M_3}{-x}p&\co{M_4}{-x}p\\
\co{M_1}yq&\co{M_2}{-y}q&\co{M_3}yq&\co{M_4}{-y}q\end{array}$$
\pq{\bf proof of 2:} If we know the $M_i$, there will be two like $M_1$
and $M_3$ above with $\co {M_1+M_3}0p$ and $M_1+M_3\not\equiv0$~mod~$q$.
Thus we can calculate $(M_1+M_3,n)$ to obtain $p$.
\pq Unfortunately, this cryptosystem is vulnerable to a chosen-plaintext
attack, even if we assume that the person trying to break the code gets
only one of the $M_i$, chosen randomly.  The attacker keeps generating
pairs $M$, $f(M)$ until he gets an $M_i$ with $(M+M_i,n)=p$ or $q$.
\section{Subset-Sum (Knapsack) problems and their uses}
\subsection{Subset-sum problems are hard}
A subset of the numbers $$\begin{array}{*{10}{r}}267&493&869&961&1000&1153&
1246&1598&1766&1922\end{array}$$ adds up to 5842. Spend a few minutes
trying to find such a subset.  Whether you succeed or not, I hope
you are convinced the task is not trivial.
\pq This is an example of a {\it subset-sum\/} problem.  In general,
we are given $n$ natural numbers $a_i$ and a target number $T$ and 
asked whether there is a $S\subset\{1,\dots,n\}$ with $$\sum_{i\in S}
a_i=T\qquad(*)$$A seemingly simpler problem is the {\it subset-sum decision\/}
problem.  For a given $a_i$ and $T$, decide whether there is an $S$
for which $(*)$ holds, without being required to identify such an $S$.
However, it can be proved that the decision problem is just as difficult
as the subset-sum problem in this sense:
\begin{Th} \label{re}
Suppose we had a method of solving the subset-sum decision
problem.  Then we could solve a subset-sum problem using the assumed
method $n$ times.\end{Th}(the $n$ is not particularly important--- the
main thing is that the number of uses of the method is not too large.)
\pq Proof: Begin by using the method to decide if
$T$ is a sum of the $a_i$--- if not, we can stop immediately.  Then
use the method to determine if $(*)$ holds for some $S\subset\{2,\dots,n\}$.
If the answer is yes, we ignore $a_1$ for the rest of the analysis.  If
the answer is no, we know we must have $1\in S$.  In this second case,
we continue by using the method to decide whether there is $S\subset\{3,\dots,
n\}$ with $$\sum_{i\in S}a_i=T-a_1$$A yes answer means we can assume $2\notin
S$, otherwise $2\in S$.\pq The idea of this proof is more important than
the specific result.  We show that one problem is as difficult as another
by showing that a method of solving the supposedly easier problem can be
used to solve another problem.  This involves constructing one or several
easier problems whose solution answers the hard problem.  We saw another
example of this idea in our discussion of the Rabin encryption system
(section~\ref{Ra}).\pq Using more elaborate versions of the techniques in
Theorem~\ref{re}, it can be shown that a method of solving
the subset-sum decision problem could be used to solve many other problems,
including:\begin{itemize}\item Factoring\item The Travelling Salesman
Problem\item Any Integer Programming Problem\item The Satisfiability
Problem\end{itemize}Don't worry if you are not familiar with the details
of these problems.  The important point is that they are all well-known
problems for which many people have been unable to find efficient solu%
tion methods, which makes it unlikely that there is a method which solves
all subset-sum decision problems efficiently (we will go into more detail
on this in section~\ref{NP}).
\pq The discussion above makes it plausible that some subset-sum problems
are difficult.  Further, there is some evidence that the ``typical''
subset-sum problem is not easy.
V.~Chvatal\footnote{``Hard Knapsack Problems,'' {\it Operations Research},
vol.~28, pp 1402--1411} has shown that if the $a_i$ and $T$
 are randomly chosen, then with high probablility (i) there will be no
$S$ for which $(*)$ holds (ii) certain simple ways of proving this will
not work.
\subsection{A proposed public-key system based on subset-sum}As an exam%
ple of an easy subset sum problem, consider the task of determining
what subset of $$\begin{array}{*{10}{r}}1&3&6&14&27&60&150&300&650&1400
\end{array}$$ adds up to 836.  The $a_i$ in this problem have the special
property that every number is greater than the sum of all preceding 
numbers ($27>1+3+6+14$, etc).  1400 clearly cannot be in the set.  If
650 is not in the set, we would be in trouble, since the sum of the
remaining numbers is $<650$, hence $<836$.  Thus 650 must be in the 
set, and we now have the task of finding numbers which add up to $836-650
=186$.  300 is too big, and the same reasoning as before shows that 150
must be in the set.  If we continue, it is easy to identify the desired
set as $\{650,150,27,6,3\}$.
\pq We began with the problem of identifying a subset of
$$\begin{array}{*{10}{r}}267&493&869&961&1000&1153&
1246&1598&1766&1922\end{array}$$ which adds up to 5842. 
What we didn't mention before was that the $a_i$ were carefully
chosen to make them directly related the $a_i$ in the easy subset-sum
problem:$$\co{267}{300(1000)}{2039}\quad\co{493}{27(1000)}{2039}\quad
869\equiv60(1000)\dots$$where 2039 is a prime chosen in
advance (larger than any of the numbers in the easy subset-sum problem)
and 1000 is an arbitrarily chosen number.
\pq To find the subset, begin by solving $\co{1000x}1{2039}$, which
gives $x=1307$.  If we let $a_i$ be the numbers in the easy problem,
the hard problem can be written as$$\co{\sum_{i\in S}(1000a_i)}{5842}{2039}$$
When we multiply by 1307, this becomes$$\sum a_i\equiv1307(5842)\equiv
1478$$It is easy to identify $\{1400,60,14,3,1\}$ as a subset
which adds to 1478, and the desired subset of the original
system is $$\{\co{1246}{1400(1000)}{2039},869\equiv60(1000),1766,
961,1000\}$$\par This would seem to give us a good public-key
system: a problem which is easy once some special information
(the 2039 and the 1000) is known, difficult without the information.
Unfortunately, the special type of subset-sum problem created in
this way can be solved even without the special information.
There is a sequence of papers showing how to solve special subset-sum
problems and proposing a refinement which, in turn, was solved by
the next paper in the sequence. This has not happened with the 
RSA system, but there is no guarantee that it won't!
\subsection{Other uses of the subset-sum problem}
The results mentioned at the end of the last section do {\it not\/}
contradict the presumed difficulty of subset-sum problems in
general.  It is only the specially constructed problems which
are known to be easy.  There are security problems other than
public-key codes for which subset-sum problems are useful.
\subsubsection{Computer passwords} A computer needs to verify a
user's identity before allowing him or her access to an account.
The simplest system would have the machine keep a copy of the
password in an internal file, and compare it with what the user
types.  A drawback is that anyone who sees the internal file
could later impersonate the user.\pq I believe this alternative
is actually implemented on some systems: the computer generates
a large number (say 500) of $a_i$.  They are stored in the 
internal file. A password is a subset of $\{1,\dots,500\}$.
(in practice, there is a program to convert a normal sequence-%
of-symbols password to such a subset.) Instead of having the
password for the user, the computer keeps the total associated
with the appropriate subset.  When the user types in the subset,
the computer tests whether the total is correct.  It does not
keep a record of the subset.  Thus impersonation is possible
only if somebody can reconstruct the subset knowing the $a_i$
and the total.
\subsubsection{Message verification} A sender (S) wants to send
messages to a receiver (R).  Keeping the message secret
is not important.  However, R wants to be sure that the message
he is receiving is not from an imposter and has not been tampered
with.  $S$ and $R$ agree on a set of $a_i$ (say 500) and a 
set of totals $T_j$ (say 200).  These numbers may be publicly
known, but only $S$ knows which subsets of the $a_i$ correspond
to which $T_j$.  The message sent by $S$ is a subset of size 100
of $\{1,\dots,200\}$.  He does this by sending 100 subsets of the
$a_i$ corresponding to the message he wants to send.
\section{Subset-Sum Problems and NP-Completeness\label{NP}}
The phrase ``NP-complete'' has an intimidating sound. In this 
section, we will first define a new problem involving formulas
in logic, called the Satisfiability~Problem (SP).  We will use
the abbreviation~(SSP) for the subset-sum problem.  Our main
results will be:\begin{enumerate}\item If there is an algorithm
which efficiently solves (SSP), it can be used to efficiently
solve (SP).\item If there is an algorithm which efficiently
solves (SP), it can be used to solve (SSP).\item An algorithm
to solve~(SP) efficiently would give efficient solutions to
factoring and many other problems.\end{enumerate}
\subsection{The Satisfiability Problem}We will use capital\label{SP}
letters $A_i$, $B_i$, to stand for logical variables. These
stand for statements like ``221 is a prime number'' or
``{\tt TH} is the most common two-letter sequence in English,''
which are either true or false, i.~e., these variables have
values of either \T\ or \F. $\sim A_i$ (``not $A_i$'') is the
statement that $A_i$ is false, so it has value \T\ if $A_i$ has
value \F, and $\sim A_i$ has value \F\ if $A_i$ has value \T.
We will also be interested in more elaborate formulas:
$$A_1\ro\sim A_2\ro\sim A_4\ro A_7\ro A_8$$
This formula says that either $A_1$ is true or $A_2$ is false, or
$A_4$ is false, etc.  The value of this formula will be \T\ unless
$A_1=\F$, $A_2=\T$, $A_4=\T$, $A_7=\F$, $A_8=\F$.  Thus, there is only
one way in which the formula will be false.
\pq Figure~\ref{EP} illustrates a  satisfiability problem.
\begin{figure}[ht]
$$\begin{array}{c}A_1\ro A_2\\ \sim A_1\ro A_5\\ \sim A_1 \ro A_3
\ro A_4\\A_3\ro\sim A_5\\A_4\ro A_5\\ \sim A_3\ro\sim A_4\\
\sim A_2\ro A_3\end{array}$$\caption{A small (SP)\label{EP}}\end{figure}
We want to assign \T, \F\ to all the variables so that
all of the formulas have value \T. Even in this small example, it 
may take you a minute or so to find such an assignment.
\subsection{Converting (SP) to (SSP)}\label{SSP}
We want to construct numbers $a_i$ and a target number $T$ so that
there is a subset adding up to $T$ if and only if there is an
assignment for (SP) which makes all the formulas
true.  Using this construction allows us to use an algorithm for
(SSP) to solve (SP).  It implies that solving (SSP) is at least as
hard as solving (SP).
\pq We will illustrate the construction using the example in
Figure~\ref{EP}.  We will have numbers $a_1,\dots,a_5$
corresponding to the logic variables $A_1,\dots,A_5$ with 
$A_i=\T$ corresponding to $a_i$ being included in the subset.
We will also need additional $a_i$, $i>5$ for technical reasons.
\begin{figure}[ht]$$\vbox{\offinterlineskip
\halign{\hfil$#{}$&$\hfil#\,$&&\vrule#
&\hfil$\,#$\cr a_1=&1&&01&&01&&00&&00&&00&&00\cr a_2=&2&&00&&00&&00&&00
&&00&&01\cr a_3=&&&&&2&&01&&00&&01&&02\cr a_4=&&&&&4&&00&&01&&02
&&00\cr a_5=&&&2&&00&&02&&02&&00&&00\cr a_6=&1&&00&&00&&00&&00&&00&&00\cr
a_7=&2&&00&&00&&00&&00&&00&&00\cr
a_8=&&&1&&00&&00&&00&&00&&00\cr a_{9}=&&&1&&00&&00&&00&&00&&00\cr
a_{10}=&&&4&&00&&00&&00&&00&&00\cr a_{11}=&&&&&1&&00&&00&&00&&00\cr
 a_{12}=&&&&&2&&00&&00&&00&&00\cr a_{13}=&&&&&3&&00&&00&&00&&00\cr
 a_{14}=&&&&&8&&00&&00&&00&&00\cr a_{15}=&&&&&&&1&&00&&00&&00\cr
 a_{16}=&&&&&&&3&&00&&00&&00\cr\noalign{\smallskip}
\dots=&&&&&&&&&\dots&&&&\cr \noalign{\smallskip}
T=&4&&04&&08&&04&&04&&04&&04\cr}}$$
\caption{\label{UGH}Subset-sum problem}
\end{figure}
\pq The subset-sum problem is shown in Figure~\ref{UGH}.
For clarity, we have divided the numbers into zones. $T$ will be a
sum of a
subset of the $a_i$ if and only if the totals within each zone
are appropriate.
Each zone corresponds to one of the logic formulas. 
For $a_1$ through $a_5$, the value in a zone is 0 if the corresponding
$A_i$ does not appear in the logic formula.  If $A_i$ does appear,
a power of 2 is used. (the reason for using powers of 2 is that
different subsets of $\{a_1,\dots,a_5\}$ will have different totals)
\pq The leftmost zone corresponds to the first logic formula in 
our example: $A_1\ro A_2$.  By making suitable
decisions about inclusion of $a_6$ or $a_7$, we will be able to get
a total of 4 in this zone, unless both $a_1$ and $a_2$ are left
out of the set, which is precisely what would make the logic formula
have value \F.
\pq The second zone corresponds to $\sim A_1\ro A_5$, which has
value \T\ unless $a_1$ is in the set and $a_5$ is not.  $a_8$,
$a_9$, and $a_{10}$ can be used to get the total for the zone
equal to 4 in any other case.
\pq Similarly, each of the other zones\footnote{
We have omitted the $a_i$ for the last three zones in Figure~\ref{UGH}. }
 has $a_i$ associated with
it which can be used to obtain the correct total except in one case.
\pq The general problem is that we want numbers which can be used to
obtain any total between 1 and $2^n$, except for one ``forbidden
total'' $M$.  [In the two zones discussed above $M$ is 4 in one
case and 3 in the other] We start with the powers of 2 from 1 to
$2^n$.  If $2^j\le M<2^{j+1}$, replace $2^j$ by the two numbers
$M-2^j$ and $M+1$.
[We did not follow exactly the procedure described in this paragraph
in constructing Figure~\ref{UGH}.]
\subsection{Converting (SSP) to (SP)}\label{S}
Suppose we have a subset-sum problem with 50 $a_i$, all between 1
and $2^{20}$, with $T<50(2^{20})<2^{26}$.  Solving the SSP may be
crudely broken into two steps:\begin{enumerate}\item Decide which
$a_i$ are in the subset.\item Verify that the sum of the chosen $a_i$ is $T$.
\end{enumerate}Our (SP) will also carry out these steps.  The first
is simple: we will have logic variables $A_1,\dots,A_{50}$ with $A_i=\T$
corresponding to $a_i$ being in the subset.  To carry out the second step,
we need to construct a set of logic formulas which acts as an ``adding
machine'' to check the total.
\pq We will represent numbers in base~2.  Since all relevant numbers are
$<2^{26}$, numbers may be represented by 26 logical variables.  For each
$1\le i\le50$, we will have variables $B_{i1},\dots,B_{i26}$.
These will represent
$a_i$ if $A_i=\T$, 0 if $A_i=\F$.  To do this, we need formulas which show
how the value of $A_i$ determines the value of all the $B_{ij}$:
$$\sim A_i\ro B_{ij}\quad A_i\ro\sim B_{ij}
\hbox{\quad if $j$th digit of $a_i=1$}$$ with the simple formula $\sim B_{ij}$
if the $j$th digit of $a_i$ is 0.
\pq Next, we need, for $2\le i\le 50$, variables $C_{i1},\dots C_{i26}$
which represent the total of the first $i$ of the numbers given by the
$B$-variables.  Formulas are needed which show the $B$-variables deter%
mining the $C$-variables.\pq We begin with a set of formulas 
${\cal S}(V,W,X,Y,Z)$ which have $Y$ get value \T\ if and only if an odd number
of $V,W,X$ have value \T. $Z$ gets value \T\ if and only if 
2~or~3 of $V,W,X$ have value \T:$$\begin{array}{ll}
V\ro W\ro X\ro \sim Y & \sim V\ro\sim W\ro Z\\
\sim V\ro W\ro X\ro Y & \sim V\ro\sim X\ro Z\\
V\ro \sim W\ro X\ro Y & \sim W\ro\sim X\ro Z\\
V\ro W\ro \sim X\ro Y & V\ro W\ro \sim Z\\
\sim V\ro\sim W\ro X\ro \sim Y &  V\ro X\ro\sim Z\\
\sim V\ro W\ro\sim X\ro \sim Y &  W\ro X\ro\sim Z\\
V\ro\sim W\ro\sim X\ro \sim Y \\
\sim V\ro\sim W\ro\sim X\ro Y \end{array}$$
If $V,W,X$ represent three one-digit numbers 
(0 or 1), the formulas ${\cal S}(V,W,X,Y,Z)$ have
the effect that $Y$ is the number in the column with the three numbers,
while $Z$ shows whether there is a number carried into the next column.
\pq We will use $D_{i1}$,\dots,$D_{i27}$, $2\le i\le50$, to keep track of
numbers being carried. Since there are no numbers carried in the rightmost
column, we have the formulas $\sim D_{i1}$.  The formulas
$${\cal S}(B_{1j},B_{2j},D_{2j},C_{2j},D_{2(j+1)})\qquad1\le j\le26$$
have the effect of making $C_{2j}$ represent the sum of the numbers $B_{1j}$
and $B_{2j}$.
To have $C_{ij}$
represent the sum of $C_{(i-1)j}$ and $B_{ij}$, we use $${\cal S}(B_{ij},
C_{(i-1)j}, D_{ij},C_{ij},D_{i(j+1)})\qquad 3\le i\le50\quad1\le j\le26$$
\pq These logic formulas together  have the effect that the $A_i$ 
determine the $B_i$,
which determine $C_{2j}$ through $C_{50j}$ successively.  This last group
of variables corresponds to the base-2 representation of the sum of 
the $a_i$ which are in the set we have chosen.  Finally, we add the formulas 
$C_{50j}$ or $\sim C_{50j}$ depending on whether the $j$th
digit of $T$ is 1 or 0.  As planned, a solution to this satisfiability
problem gives a solution to the subset-sum problem (look at which $A_i$
have value \T), which implies that a method of solving (SP) can be used
to solve (SSP).\pq The (SP) we have constructed is rather large, 
involving approximately $15(26)(50)$ formulas.  However, the rate of
growth if we had more $a_i$ with a larger upper limit is not too bad.
\subsection{Cook's Theorem}It is more important to understand the
general idea of what we did in section~\ref{S} than to get involved in
the details of the construction of the ``adding machine.'' We used 
logical variables (the $A_i$) to represent our guess as to what a solution
to the (SSP) was, then constructed a set of formulas to verify the
guess was correct.\pq You should be able to convince yourself that a
similar thing could be done with a factoring problem.  We could have
variables $A_i$ and $B_i$ represent our guesses as to two factors, then
construct a ``multiplication machine'' to verify that the product is
what we want.  Thus an efficient algorithm for (SP) would lead to an
efficient algorithm for factoring\footnote{Unlike (SSP), nobody has
been able to show that an algorithm for factoring would give an algorithm
for (SP).}.\pq Many other problems can be viewed as making some guesses
as to what the correct answer is, with the process of verification
relatively easy. An example might be an integer programming problem:
$$\begin{array}{l}\max cx\\Ax\le b\\x_i=0\ro1\end{array}$$
We ask if there is a feasible solution with objective function value $>K$.
For a given $x$, it is easy to check that it satisfies all the problem
constraints and tell if the value is big enough.\pq The detailed 
construction in section~\ref{S} was intended primarily to convince
you that, if a verification can be done efficiently, it can be 
simulated by a set of logic formulas.  It should make you willing to
believe\begin{Th}[Cook]Any ``guess and verify'' problem can be converted
to a satisfiability problem. Thus, an efficient algorithm for (SP) can
efficiently solve any ``guess and verify'' problem.\end{Th}
\subsection{Terminology} The concept we have vaguely described as
solving efficiently is technically known as ``polynomial time.''
The types of problem that can be considered as ``guess and verify''
are called NP (for Nondeterministic [the guessing stage] Polynomial
[the verification stage]).  Cook's Theorem says that (SP) is as hard
as any NP problem--- the usual terminology is to say (SP) is NP-complete.
Since we showed in section~\ref{SSP} that (SSP) could be used to solve
(SP), we essentially proved that (SSP) is also NP-complete.
\pq {\it Computers and Intractability}, by Garey and Johnson, is strongly
recommended for more information.
% This is part 2 of a LaTeX file of cryptography notes.  It will not
% run correctly unless concatenated with part 1.
%
\section{A probabilistic test for primality}\label{prim}
Suppose we want to test whether 247 is a prime number.  Recall two facts
about prime numbers $p$:\begin{enumerate}\item $\co{a^{p-1}}1p$ if 
$a\not\equiv0$.\item If $\co{a^2}1p$, then $a\equiv1$ or $a\equiv-1$.
\end{enumerate} Suppose we randomly choose $a=2$ and test for consistency
with these conditions.  Since $\co{2^{246}}{220}{247}$ we can conclude
immediately that 247 is {\it not\/} a prime.\pq Perhaps we were lucky
with $a=2$.  If we try $a=27$, we get $\co{a^{246}}1{247}$.  However,
$$27^{246}\equiv \left(27^{123}\right)^2\hbox{ and }\co{27^{123}}{170}{247}$$
which is inconsistent with the second condition, again implying 247 is not
a prime.\pq Not every choice of $a$ is inconsistent with the conditions.
For example, $160^{123}\equiv-1$ (hence $160^{246}\equiv1$) and 
$178^{123}\equiv1$. However, the fact that some choices of $a$ give a
proof that a number is not prime suggests the following test:
\pq{\bf Rabin's Primality Test.} Let $p-1=2^km$, where m is an odd number.
Choose $a$ at random.  Compute the sequence $$a^m\quad a^{2m}\quad a^{4m}
\dots a^{p-1}\qquad\hbox{mod $p$}$$This sequence is consistent with $p$
being a prime if $a^m\equiv1$ or if the sequence has  $-1$ at some point,
followed by 1 for all subsequent terms.  In all other cases, $a$ provides
a proof that $p$ is not prime (this is usually described by saying that
$a$ is a {\it witness\/} that $p$ is not prime).  Repeat this test for
some number of random choices of $a$, and conclude that $p$ is a prime
if none of the chosen $a$ is a witness.
\pq Two features of the test should be emphasized.  It does not provide
an absolute guarantee that $p$ is a prime, only that it is probably a prime
(we will analyze exactly how probable in the next section).  Secondly,
when we know $p$ is not a prime, we do not know what its factors are---
factoring is much more difficult than testing for primality.\pq[A different
probabilistic test is described near the end of the RSA paper.]
\subsection{Analysis of the Rabin test}
We will calculate how many $a$ are witnesses that 247 is not a prime.  Our
analysis will make use of the fact that $247=13(19)$ and that 2 is a
primitive root for both 13 and 19.  However, it should be emphasized that
this information (which will not be available in general) was not used
when we did the test itself.
\pq How many $a$ satisfy $\co{a^{123}}1{247}$?  We must have 
$\co{a^{123}}1{13}$
and $\co{a^{123}}1{19}$.  Let $\co a{2^x}{13}$.  Then we must have $123x$
divisible by 12.  This gives the possible values $0,4,8$ for $x$, which
implies $a\equiv1,3$,~or~9 mod 13.  Similarly, if $\co a{2^x}{19}$, $123x$
must be divisible by 18, which leads to $a\equiv1,7$,~or~11 mod~19.  (we
actually found 178 above by solving $\co a9{13}$ and $\co a7{19})$
The 3 choices mod~13 and mod~19 imply there are 9 $a$ with $a^{123}\equiv1$.
\pq How many $a$ satisfy $\co{a^{123}}{-1}{247}$? If $\co{2^{123x}}{-1}{13}$,
we must have $\co{123x}6{12}$, which leads to $a\equiv4,7$, or~17 mod~13.
Similarly, we get $a\equiv8,18$, or~12 mod~19.  Thus we get 9 $a$ satisfying
this condition.\pq If we choose $1\le a\le246$ at random, the chances of
getting an $a$ that is not a witness are $18/246\approx.073$.  If we do the
test 5~times, the chance of incorrectly concluding 247 is a prime is 
$\approx2(10^{-6})$.\pq [We actually did more work than necessary, ident%
ifying the exact set of numbers which would lead to a wrong conclusion.
If we only want to count how many numbers there are, we could make use 
of observations such as that, for any $k$, an equation $\co{123x}k{12}$
will either have 3 solutions or no solutions.]
\begin{Th}[Rabin]If $p$ is not a prime, at least $3/4$
of $1\le a\le p-1$ are witnesses.\end{Th}
This implies that for any non-prime $p$, the chance of being incorrectly
identified after 5 tests is $\le4^{-5}<.001$.


==============================================================================

Newsgroups: sci.math
From: ceblair@ux1.cso.uiuc.edu (Charles Blair)
Subject: CRYPTOGRAPHY NOTES (part 2 of 2)
Date: Tue, 8 Sep 1992 13:26:56 GMT

%   This is the second part of some notes on cryptography.  It is a LaTeX
%file.  To make it work, you must concatenate with the first part, or at
%least the part up to but not including \section{Encryption Systems}
%
\section{Probabilistic Encryption\label{pro}}
[References to ``the paper'' in this section are to ``Probabilistic
Encryption,'' in {\it Journal of Computer \& System Sciences\/}~28,
pp.~270--299. I have also used {\it Primality and Cryptography},
by E.~Kranakis]\pq
So far, the public key systems have been functions $f$ such that 
the message $M$ presumably cannot be computed from the encoding $f(M)$.
A further concern arises as to whether, even if the adversary cannot
identify $M$ exactly, he may be able to obtain some partial information
about $M$, for example tell whether $M$ is an even number, a square,
a power of 2, etc.\pq An extreme case of this would be a scenario
in which the adversary knows the message is one of two possibilities,
$M_1$ or $M_2$. Since we have been assuming that the function $f$ is
easy to calculate, all the adversary needs to do is compare $f(M_1)$ and
$f(M_2)$ with the ciphertext.
\pq Probabilistic encryption is a system designed to avoid these problems.
Instead of $f(M)$ being a single number, the calculation of $f(M)$ involves
the sender doing some things randomly during the calculation, so that $M$
has many different encryptions.  Indeed, the probability should be very
close to 1 that if the same message is sent twice, the encryptions should
be different.
\subsection{The Goldwasser-Micali encryption system}
As in many previously discussed systems, the person receiving messages
chooses two primes ($\sim100$ digits) $p,q$ and announces $n=pq$.
This system is concerned with whether, for a given number $a$, there is
$x$ with $\co{x^2}an$. Such $a$ are called {\it squares\/} or (in most
books and papers) {\it quadratic residues}. For technical reasons, when
we refer to squares mod $n$, we will exclude $a$ which are divisible by
$p$ or $q$.
The following facts are easy to prove, in some cases using primitive roots.
\begin{Le} If $a,b$ are squares, then $ab$ is a square.
If $a$ is a square and $b$ is not a square, then $ab$ is not a square.
\label{prod}\end{Le}
\begin{Le} $a$ is a square mod $n$ if and only if it is a square mod $p$
and a square mod $q$.\label{kn1}\end{Le}
\begin{Le} Let $h=\frac{p-1}2$.  If $a$ is a square mod $p$, $\co {a^h}1p$.
If $a$ is not a square, $a^h\equiv-1$.\label{kn2}\end{Le}
This implies that, if $p$ and $q$ are known, it is easy to decide whether
$a$ is a square.  The encryption system depends on the assumption (called
QRA in the paper [p.~294]) that this problem is very difficult if $p,q$
are unknown.\begin{Le} $1/2$ of the numbers from 1 to $p-1$ are squares
mod $p$.     Take the numbers from 1 to $n$ and leave out those divisible
by $p$ or by $q$. Divide the remaining $(p-1)(q-1)$ numbers into four groups 
according to whether they are squares or not mod $p$ and also mod $q$.
There are $(p-1)(q-1)/4$ numbers in each group.\end{Le}
\pq The numbers which are not squares mod $p$ and also not squares mod $q$
are called {\it pseudo-squares}.  Example: If $p=5$, $q=7$, the squares
mod~35 are 1, 4, 9, 16, 29, 11 ($29\equiv8^2$, $11\equiv9^2$; 
note we don't include 25 and 14, because
they're divisible by $p,q$).  The pseudo-squares must be congruent to
2 or 3 mod~5 and to 3, 5, or 6 mod~7.  Thus the pseudo-squares are
17, 12, 27, 3, 33, 13.
\pq The encryption system is primarily concerned with the union of the
set of squares and pseudo-squares--- this set is unfortunately denoted
both by $Z^1_n$ (p.~291) and by $Z^{{}+1}_n$. Since exactly half the
members of $Z^1_n$ are squares, the crude idea of saying ``this is a
square'' all the time will
only be right half the time.  (QRA) says that no algorithm that runs
in a reasonable amount of time can do much better than this.  [the precise
definitions of ``reasonable'' and ``much better'' are what require the
concepts of circuits of size $k$ and ``$\epsilon$-approximating'']
\pq In addition to announcing $n$, the person receiving messages announces
one pseudo-square $y$.  To send a sequence of 0's and 1's, the sender 
converts them into numbers as follows: for each number in the sequence,
an $x$ is chosen {\it at random}.   0 is converted into $x^2$ mod~n,  1 is
converted into $yx^2$.  Each 0 or 1 in the sequence can be converted
(depending on the choice of $x$) into one of $(p-1)(q-1)/4$ different
numbers.  If the message is of length 500 (about one line of ordinary
text), and $p,q\approx10^{100}$, the message can be encoded into 
$~(1/4)10^{100000}$ different possible ciphertexts.
\pq By Lemma~\ref{prod}, 0's are converted to squares, 1's are converted
to pseudo-squares.  Since the receiver knows $p,q$, Lemmas~\ref{kn1}
and~\ref{kn2} show he can efficiently decode the message.
\pq In the subsequent sections, we will give the essential ideas
of Goldwasser \& Micali's proof that (assuming QRA) this system will
prevent the adversary from 
obtaining any partial information about the plaintext.
\subsection{Weak laws of large numbers}
Both the encryption algorithm and the hypothetical algorithms used by
the adversary involve random events.  We will need a theorem that says
that, if an event with probability $p$ is tried $r$ times, the chance that
the number of successes is not close to $pr$ is small.  The paper uses%
\footnote{The usual central limit theorem cannot be used because it does
not tell you how large $r$ must be for the normal distribution to give
a good estimate.}
(p.~293) \begin{Le}Let $S_r$ be the number of successes in $r$ tries.
For any $\psi$ $$\Pr\left(\left|\frac{S_r}r-p\right|
>\psi\right)<\frac1{4r\psi^2}$$
\label{weak}\end{Le}
\par{\bf Proof:} $S_r$ is a random variable, which is the sum of $r$
independent random variables, each having value 0 or 1.  Let $V$ be the
variance of $S_r$.  Each of the 0--1 variables has variance $\le1/4$,
so $$r^2\psi^2\Pr(|S_r-rp|>r\psi)<V\le\frac r4$$
\pq Lemma \ref{weak} provides a very rough estimate of the probability.
An improvement requiring much more work is:
\begin{Le} \label{strong}With the same notation as Lemma~\ref{weak}, 
\begin{eqnarray*}&&\Pr\left(\frac{S_r}r\ge p+\psi\right)\\&&
\le\frac1{\sqrt{2\pi r(p+\psi)(1-p-\psi)}}
\left(\frac{(1-p)(p+\psi)}\psi\right)\exp\left(
-\frac{r\psi^2(1+\psi)}{2(1-p)(p+\psi)}\right)
\qquad(*)\end{eqnarray*}\end{Le}
\par For comparison, if $p=.5$, $r=1000$, the probability that there are
$\ge520$ successes is .1087.  Lemma~\ref{weak} gives%
\footnote{We divide by 2 to eliminate the probability of $\le480$.}
an upper limit of .3125,
while Lemma~\ref{strong} gives .1498.  (these figures courtesy of 
Mathematica)\pq One reason the paper does not
use Lemma~\ref{strong} is that it does not give a simple formula for
how large $r$ would have to be in terms of the other quantities. We will
not use this result later, and you should skip to section~\ref{Sa}
unless you like to manipulate formulas.
\pq{\bf Proof:} We will assume $pr+r\psi$ is integer.
From the binomial theorem:
\begin{eqnarray*}
\Pr(S_r\ge rp+r\psi)&=&\sum_{i\ge pr+r\psi}
\left(\begin{array}{c}r\\i\end{array}
\right)p^i(1-p)^{r-i}\\&\le&\left(\begin{array}{c}r\\pr+r\psi\end{array}
\right)p^{pr+r\psi}(1-p)^{r-pr-r\psi}(1+\alpha+\alpha^2+\dots)\\
&&\hbox{where }\alpha=\frac{p(r-pr-r\psi)}{(1-p)(pr+r\psi+1)}
\end{eqnarray*}$p+\psi\le1$ implies $p-p\psi-p^2>0$ and
$$\sum\alpha^i=\frac1{1-\alpha}=\frac{(1-p)(pr+r\psi+1)}{r\psi+1-p}\le
\frac{(1-p)(p+\psi)}\psi$$ which gives the second factor of $(*)$. We
use Stirling's formula on the binomial coefficient and group it with the
powers of $p$ and $1-p$ to obtain:
$$\left(\frac 1{\sqrt{2\pi r(p+\psi)(1-p-\psi)}}\right)
\left(\frac p{p+\psi}\right)^{pr+r\psi}
\left(\frac{1-p}{1-p-\psi}\right)^{r-pr-\psi r}\quad(**)$$
The first factor of $(**)$ is the first factor of $(*)$.  We
obtain upper bounds on the rest of $(**)$, using $$-A-\frac{A^2}{2(1-A)}
\le \ln(1-A)\le-A-\frac{A^2}2$$(the lower bound on $\ln(1-A)$ 
involves a geometric series)
\begin{eqnarray*}(pr+r\psi)\ln\left(1-\frac\psi{p+\psi}\right)
&\le&-r\psi-\frac{r\psi^2}{2(p+\psi)}\\
(pr+\psi r-r)\ln\left(1-\frac\psi{1-p}\right)&\le&
\frac{(r-pr-r\psi)\psi}{1-p}+
\frac{(r-pr-r\psi)\psi^2(1-p)}{2(1-p)^2(1-p-\psi)}\\
&=&r\psi-\frac{\psi^2r}{1-p}\left(-1+\frac12\right)\end{eqnarray*}
Adding these and using $\exp$ gives the remaining factor of $(*)$.
\subsection{The magic of sampling\label{Sa}}
We have $10^6$ envelopes.  Inside each envelope is a piece of paper with
0 or 1 written on it.  If we want to know exactly how many envelopes have
each number, we have to open them all.  Suppose we want to estimate the
fraction of the envelopes of each kind, and we want the proportion to be
accurate to within .05.  Now we need only open $9(10^5)$ envelopes.
\pq The situation changes dramatically if we only want to estimate the
proportion with high probability.  If we are willing to accept a .01
probability of an error $>.05$, Lemma~\ref{weak} implies we only need
to open a randomly chosen sample of $10^4$ envelopes\footnote{Lemma
\ref{strong} and Mathmatica suggest 400 envelopes are enough.}.
\pq The special feature of problems involving squares and pseudo-%
squares is that sampling is possible.  We saw in our discussion of
the Rabin system that every number mod $n$ has four square roots.
Thus if we choose one of the $(p-1)(q-1)$ numbers $x$ not divisible%
\footnote{Even though $p,q$ are unknown, the gcd of $x,n$ can
be computed.} by $p$ or $q$
 and compute $x^2$~mod~n, each square has a $(p-1)(q-1)/4$
chance of being chosen.  It is also important that it is possible to
sample from $Z^1_n$ (the set of squares and pseudo-squares) even if
$p,q$ are not known.\begin{Le} \label{Jac}There is an efficient algorithm for
deciding if $a\in Z^1_n$.\end{Le}The proof of this is difficult, 
involving ``quadratic reciprocity'' and the ``Jacobi symbol.''  The
algorithm itself is not that complicated, and is given in the RSA 
paper.\pq Given this lemma, we can sample in $Z^1_n$ by choosing $x$
at random and testing if it is in the set.  If not, another $x$ is
chosen.  Since roughly half of $1\le x\le n$ is in $Z^1_n$, this won't
take too long.\pq The different sampling possibilities we have discussed
so far have all assumed that only $n$ was known.  If we are given a single
pseudo-square $y$, we can sample among all pseudo-squares by calculating
$yx^2$ for $x$ randomly chosen.
\pq The possibility of doing these various kinds of sampling
is closely related to properties 2(a) and (c) in the paper (p.~277).
\subsection{Determining algorithm performance by sampling}\label{samp}
We are interested in algorithms for deciding whether a given number
is or is not a square.  As with the algorithm in Section~\ref{prim},
there is some probability that, for a given input $a$, the algorithm
may give the wrong answer.
\pq Let $p_a$ be the probability that a given algorithm gives the correct
answer for input $a$.  We are also interested in $p_S$, which is the
average of $p_a$ over all squares $a$, and $p_{PS}$, the average over
all pseudo-squares, and $p_Z$, the average of $p_a$ over all $a\in Z^1_n$.
\pq If we are given an algorithm, we can easily determine $p_S$ by
running it with input $a=x^2$ on a sample of randomly chosen $x$ and
counting the number of times the algorithm answers ``this is a square.''
\pq The procedure for determining $p_Z$ is more elaborate.  Suppose we
have an algorithm for which $p_S=.6$.  Using Lemma~\ref{Jac}, generate
a sample of 100 members of $\zo$, and run the algorithm on each of them.
Suppose we get the answer ``this is a square'' 65 times.  There are 
$\sim50$ squares in the sample, on which there have been .6(50) correct
responses and 20 incorrect.  Pseudo-squares have been identified as 
squares $65-30=35$ times, which suggests $\ps\approx15/50$.  Finally
$p_Z=(p_S+\ps)/2\approx.45$.\pq Lemma~\ref{weak} or \ref{strong} can
be used to determine the probability that these estimates come within
a specified amount.
\subsection{Two versions of QRA}
\begin{enumerate}\item There is no efficient algorithm for distinguishing
squares from pseudo-squares with $p_a>1-\epsilon$ for all $a\in \zo$.
\item There is no efficient algorithm with $p_Z>.5+\epsilon$
\end{enumerate}\par It would seem that (1) is not as strong as (2). Note
that (2) would rule out an algorithm with $p_S=.9$ and $\ps=.2$.  This 
would be something that says ``this is a square'' most of the time, 
occasionally correctly identifying a pseudo-square.  However, the 
paper (p.~293) shows that (1) implies (2).
\pq Suppose we are given an algorithm.  We estimate $p_S,\ps,p_Z$ with
high probability using the techniques in Section~\ref{samp}.
To take a specific example, we will assume we find $p_S=.6$, $\ps=.45$.
We want to test whether $a$ is a square.  Run the algorithm on $ax^2$
for 1000 randomly chosen $x$. If $a$ is a square, the algorithm will say
``this is a square'' $\approx600$ times.  If $a$ is a pseudo-square, the
answer will be ``this is a square'' $\approx550$ times.
\subsection{Knowing a pseudo-square does not help much}
QRA talks about the ability to identify squares when only $n$ is known.
In the proposed encryption system, a pseudo-square $y$ is also announced.
The paper shows (p.~295) that this does not make the problem easier.\pq
Suppose we have an algorithm which takes as input $a,y$ and tries to
decide if $a$ is a square. Assume $p_Z=.55$ whenever $y$ is a pseudo-square.
Choose $y\in\zo$ at random, then use the techniques from Section~\ref{samp}
to estimate $p_Z$.  Since half the numbers in $\zo$ are pseudo-squares,
you will quickly find a $y$ for which $p_Z=.55$.
\subsection{The inability to distinguish two plaintexts}
Theorem~5.1 of the paper addresses the issue we mentioned at the
beginning of section~\ref{pro}.  It shows that if we have an algorithm
which
can identify messages $m_1$ and $m_2$
and  efficiently tell the difference between an en%
cryption of $m_1$ and an encryption of $m_2$, then we could construct
an algorithm which efficiently distinguishes squares from pseudo-squares.
Thus (QRA) implies we cannot tell the difference between $m_1$
and $m_2$. \pq {\bf Proof:}\footnote{The argument we give is a
simplification of the one in the paper, in that we do not use the
``sampling walk.''  The more complicated argument seems to be necessary
to analyze encryption systems in general, as opposed to those based
on squares and pseudo-squares.} Suppose we are trying to decide
whether $a\in\zo$
 is a square and that the two distinguishable
 messages are \begin{eqnarray*}m_1&=&01001011\\m_2&=&11101101 \end{eqnarray*}Choose
8 $x_i$ randomly and consider the sequences $$\vbox{\halign{&\hfil\quad$#$\cr
x_1^2&ax_2^2&x_3^2&x_4^2&ax_5^2&x_6^2 &ax_7^2&ax_8^2\cr ax_1^2&ax_2^2&ax_3^2&x_4^2&ax_5^2&ax_6^2&x_7^2&ax_8^2\cr}}$$
If $a$ is a pseudo-square, these will be randomly chosen encodings
of  $m_1$ and $m_2$.  In this case, the performance of our assumed
algorithm on the two sequences (averaged over repeated random choices
of $x_i$) will be different.  If $a$ is a square, both sequences
will be randomly chosen encodings of the message consisting of
all 0's, so the algorithm's response on average to the two sequences
will be identical.
\subsection{Semantic Security} Theorem~5.2 of the paper shows that
there is no property of the plaintext message which can be efficiently
estimated by looking at the ciphertext.  Typical properties might
be ``the last bit of the plaintext is 0'' or ``the number of 1's
is twice as much as the number of 0's.''  In general, a property
is defined in the paper as the value of a function $f(m)$ which
takes a message as input and gives a number as output.  If $f(m)$
is constant for all $m$, prediction of $f(m)$ is trivial.  Similarly,
if $f(m)$ is almost constant for almost all $m$, there is a simple
algorithm which will be close to right with high prob% ability.
 \pq We wish to show that, except in the special cases we've  mentioned,
there is no efficient algorithm which will predict $f(m)$ from
the ciphertext for $m$.  If there were,
  we  could run our algorithm to estimate $f(m)$
on the ciphertext from randomly generated $m$ until we found $m_1$,
$m_2$ on which the algorithm behaved differently.  But this would
contradict the result of the previous section.
\pq [The paper points out that it is not assumed that $f(m)$ is
an easily computable function.  I think this is a minor issue.
 The theorem really discusses the capabilities of a an easily computable
program for estimating $f$.]
\subsection{How to play poker over the telephone}
We will not analyze an entire game of poker, but just the task
of each player [we will assume only two players]
 getting dealt cards so that (i) each player gets his cards at
random, with all cards equally likely (ii) neither player knows
what his opponent has (iii) the players cannot get the same cards.
You will probably appreciate the procedure more if you first try
to devise a way of doing this yourself.\pq
Several previous attempts to use cryptographic devices for this
purpose were flawed\footnote{R.\ Lipton, ``How to cheat at mental
poker,'' {\it Proceedings of AMS Short Course on Cryptography}}.
 The elaborate procedure we describe is based on some number-theory
tools developed in section~\ref{Ra}
 and earlier in this section:\begin{enumerate}
\item If $n=pq$ and $a$ is a square mod~$n$, it has four square
roots. If we know roots $r_1,r_2$ with $r_1\not\equiv\pm r_2$,
we can find $p$, $q$.\item If $\co p34$, $a$ is a square mod $p$
if and only if $-a$ is not a square (Lemma~\ref{kn2}).
If we also have $\co q34$,
then $a\in\zo$ if and only if $-a\in\zo$.\item We can test whether
or not $a\in\zo$ without knowing $p,q$.\end{enumerate}
\pq Two techniques are used repeatedly.  They are also of interest
in other applications.\begin{Th}[random numbers]\label{R} B can generate
a random number so that A does not know its value now, but can
verify it later. \end{Th}A ``first try'' might be for B to generate
a random number and give an encryption of it to A, with the key
 revealed for verification later.  This does not work, since
A cannot be sure that B chose his number at random.\pq To insure
randomness, A gives B a second number (which A is supposed to
choose at  random) after receiving B's encryption, 
and the number used by B is the ``exclusive or'' of the two:
\begin{center}\begin{tabular}{r} A chooses 0110001\\B chooses 1011011\\
\cline{1-1}B uses 1101010\end{tabular}\end{center}
Even if one of the players does not choose his number at random,
the result will be random as long as the other player does.
\begin{Th}\label{UU}B can ask A a question related to $n$.  The answer to
this question may or may not allow B to factor $n$.  At the time
the question is asked, A cannot tell whether the answer he gives
B is useful or  useless, but this can be verified later.\end{Th}
{\bf Proof:} A chooses primes $\co{p,q}34$, and announces $n=pq$.
 Using the technique of Theorem~\ref{R}, B generates a random $x$, and
will ask A for a square root of $a\equiv x^2$.  At the time the
question is asked, A will know $a$ but not $x$.  B is allowed to
specify whether the square root A gives him is or is not in $\zo$.\pq
 If $x\in\zo$ and B specifies that the square root is in $\zo$,
A will give B $\pm x$, which is useless.  B can get useful information
by specifying that the square root is not in $\zo$.  If $x\not\in\zo$,
the square root in $\zo$ will be useful, and the other will be
useless.  \pq Since $x$ is  randomly chosen, and half the possible
$x$ are in $\zo$ and half are not, A will not be able to guess
right more than half the time whether he is being asked for useful
or useless information.
\subsubsection*{The procedure}
\begin{enumerate}\item A announces $n_1,\dots n_{52}$, each of
which is a product of two large primes $\co{}34$.  He encodes the
names of the different cards using different $n_i$ and also announces
these.  [if B finds the factors
of one of the $n_i$, it does not help him identify the other cards]
B does the same thing using $m_1,\dots m_{52}$.
\item To get a card, B asks A one question for each $n_i$, using
the procedure of Theorem~\ref{UU}.  51 of the questions will be useless.
 The  useful question allows B to decode the name of the card he
receives. [it is crucial that A will be able to verify the uselessness
of the other 51 questions after the game.]
\item B deletes the $m_i$ corresponding to the card he received
(this ensures A will not get this card).
\item A gets a card by asking 51 questions about the remaining
$m_i$,  of which 50 are useless.
He deletes the $n_i$ corresponding to this card.
\item If B gets a second card, he asks 51 questions.  He avoids
getting the same card twice by not asking a useful question about
the same $n_i$ as the first time.\end{enumerate}
This procedure is too cumbersome to be practical, but it is a good
example of the kinds of things that can be done using cryptographic
procedures.  Current research focusses on other tasks involving
exchanges of encrypted and partially encrypted information between
two players. 
\section{Pseudo-random number generators}[This section is based on Blum, Blum,
\& Shub, ``A simple unpredictable pseudo-random number generator,''
{\it SIAM J. Computing\/}~15, 364--383.]\pq
Many programs (e.~g., simulations, one-time-pads) make use of numbers
that are supposed to be random.  A genuine source of randomness might
be a subroutine that made calls on something like a built-in Geiger
counter.  We will be concerned with algorithms that produce a sequence
of numbers (usually 0's and 1's) which appears random (precise definition
will be given later).\pq A typical example of such an algorithm is the
function {\tt rand()} in the C programming language. Each call updates
an internally maintained $N$ using the formula
$$N=N*1103515245+12345\quad\hbox{mod } 4294967296=2^{32}$$with the 
output given by $2^{-16}N$~mod~$2^{15}$.\pq I recently wrote a program
to roll dice which involved using {\tt rand()}~mod~6.  In over 100
calls, it never happened that the same number occurred on two consecutive
 rolls, even though this should have happened about $1/6(100)$ times!
This suggests this particular generator has some problems.\footnote{Knuth
suggests that a better way to obtain a random number between 0 and~$k-1$
is to use $k\,${\tt rand()}${}/M$, where $M$ is the maximum value of {\tt
rand()}.}\pq In this section, we will present random number generators
for which it can be proved (given assumptions like (QRA)) that such
problems will not occur.
\subsection{The Quadratic Generator} Let $n=pq$, where $p,q$ are primes
$\co{}34$.  For each prime, $a$ is a square if and only if $-a$ is not
a square (Lemma~\ref{kn2}).
This implies that, if $\co x{\pm a_1}p$ and $\co x{\pm a_2}q$,
there will be exactly one choice which makes $x$ a square mod~$n$.
Hence, if $b$ is a square mod~$n$, exactly one of its four square roots
will also be a square.  This {\it principal\/} square root will be
denoted by $\sqrt b$.
\pq The quadratic generator uses a randomly chosen square $x$ (called the 
{\it seed\/}) not divisible by $p$ or $q$ to generate a sequence of
0's and 1's ({\it bits\/}).  The sequence is $a_i$~mod~2, where $a_0=x$
and $\co{a_{i+1}}{\sqrt{a_i}}n$:$$x\ \hbox{mod }2\quad\sqrt x\hbox{ mod }2
\quad\sqrt{\sqrt{x}}\hbox{ mod }2\quad\dots$$
(from a practical point of view, it is simpler to generate the sequence
starting with the last number and squaring)\pq
As a small example with $n=589=19(31)$ and $x=81$, the sequence of $a_i$ is
$$81\quad9\quad586\quad175\quad112\quad443\quad214\quad237\dots$$(note
that $\sqrt 9=-3$, not~3) which gives the sequence of bits 11010101.
\subsection{The Next Bit Theorem}
It would certainly be undesirable if there were an efficient algorithm
which took as input the first $k$ bits of the sequence from the generator
and guessed the $(k+1)$-st bit with probability much greater than $1/2$.
We say a generator satisfies the {\it Next Bit Condition\/} if there is
no such algorithm.\begin{Th}If (QRA) is true, the quadratic generator
satisfies the Next Bit Condition.\end{Th}{\bf Proof:} We will show that
an algorithm that could predict the $(k+1)$-st bit could be used to
distinguish squares from pseudo-squares mod~$n$.\pq Let $b\in\zo$. The
sequence of length $k$ $$b^{2^k}\quad b^{2^{k-1}}\dots b^4\quad b^2$$
can be considered as coming from the quadratic generator with seed the
first term of the sequence.  If we take this sequence mod~2 and give
it to our predictor, we would get a guess as to whether $$\co
{\sqrt{b^2}}{0\hbox{ or }1}2$$which has probability $>1/2$ of being
right.  The principle square root of $b^2$ is $b$ if $b$ is a square,
$n-b$ if $b$ is a pseudo-square.  Since $b\not\equiv n-b$~mod~2, the
information from the predictor gives us a guess as to whether $b$ is
a square.
\subsection{The Efficient Test Theorem}
When we are given a sequence of bits from a pseudo-random number generator,
we often test the quality of the generator by doing things like counting
the fraction of 0's, the fraction of subsequences of the form 111, etc.
\pq A {\it test\/} is defined to be an efficiently computable function
$T$ which takes as input a sequence of bits of length $m$ 
and gives as output a number between 0 and 1.  Define
\begin{eqnarray*}A_r&=&\hbox{Average over all sequences $s$ }\{T(s)\}\\
A_g&=&\hbox{Average over $s$ from the generator }\{T(s)\}\end{eqnarray*}
These averages both involve finite operations--- $A_r$ involves adding
up $T(s)$ over the $2^m$ possible $s$ and dividing.  Similarly $A_g$
deals with an average over all possible seeds (presumably the number of
possible seeds is much less than $2^m$).
\pq It would take too much time to calculate $A_r,A_g$ exactly, but they
can be estimated with high probability using the sampling ideas in
section~\ref{Sa}.
\pq A generator is said to {\it satisfy\/} the test $T$ if $A_g$ is close
to $A_r$, i.~e., $T$ cannot tell the difference between sequences from
the generator and genuinely random sequences. [we are being deliberately
vague about the precise definition of ``close.'']
\begin{Th} If a generator satisfies the Next Bit Condition, it satisfies all
efficiently computable tests $T$.\end{Th}
{\bf Proof:} We will show that, if we had $T$ with $A_r$ significantly
different from $A_g$, then for some $k$, $T$ could be used to predict
the $(k+1)$-st bit from the first $k$ bits with probability somewhat
larger than $1/2$. This would contradict the Next Bit Condition.
\pq If $s$ is a sequence of $i$ bits, let $f_s$ be the fraction of all
possible seeds whose first $i$ bits are $s$.  For some $s$, we may have
$f_s=0$.  Note that$$A_g=\sum_sf_sT(s)$$where the sum is over all $s$ of
length~$m$.\pq
The proof involves two steps:\begin{enumerate}\item Identify a $0\le
k\le m-1$ such that the behavior of $T(s)$ depends in a significant way
on the $(k+1)$-st bit of $s$.\item Use $T$ to make a prediction for
the $(k+1)$-st bit.\end{enumerate}
\pq The proof of step~1 uses ideas similar to the ``sampling walk'' used
to prove Theorem~5.1 in the Goldwasser-Micali paper.  Define
$$A_i=\sum_{s,t}f_s2^{i-m}T(s\circ t)$$where the sum is over all $s$ of length
$i$ and $t$ of length $m-i$, with $\circ$ meaning to combine
$s$ and $t$ to create a sequence of length $m$.
$A_i$ is the expected value of $T$ applied to a sequence in which the
first $i$ bits come from the generator (using a randomly chosen seed),
with the remaining bits coming from a genuinely random source.
\pq Note that $A_0=A_r$, $A_m=A_g$, and that all $A_i$ can be 
estimated with high probability using sampling. Since\begin{equation}
|A_r-A_g|\le\sum_1^{m}|A_i-A_{i-1}|\hbox{ there is $k$ with }
|A_{k+1}-A_{k}|\ge |A_r-A_g|/m\label{kch}\end{equation}
  This completes step 1.\footnote{Instead of estimating
all the $A_i$, we could begin by estimating $A_{.5m}$.  We would next
estimate either $A_{.75m}$ or $A_{.25m}$, depending on whether $A_{.5m}$
was closer to $A_0$ or $A_m$.}
\pq In step 2, we are concentrating on a specific sequence $s$ of length
$k$, where $k$ satisfies (\ref{kch}).  We
wish to use the behavior of $T$ to predict whether the $(k+1)$-st bit
should be 0 or 1.  Intuitively, we ask $T$ which of the two possibilities
would make the sequence look more random.  
\pq We will need to look at the analogues of the averages $A_k$ and
$A_{k+1}$, restricting attention to those sequences which begin with
$s$:\begin{eqnarray*}A_k(s)&=&\sum_t2^{k-m}T(s\circ t)\\
A_{k+1}(s)&=&\sum_t(f_{s\circ0}/f_s)2^{k+1-m}T(s\circ0\circ t)+\\
&&\sum_t(f_{s\circ1}/f_s)2^{k+1-m}T(s\circ1\circ t)\end{eqnarray*}
The definition of $A_{k+1}(s)$ is based on the idea that $s\circ0$
and $s\circ1$ are the only sequences of length $k+1$ which begin with $s$.
Note that, for $i=k$~or~$k+1$, $A_i=\sum_sf_sA_i(s)$, where the
sum is taken over all $s$ of length $k$.
\begin{eqnarray*}\hbox{Define\quad}A_{s,0}&=&\sum_t2^{k+1-m}
T(s\circ0\circ t)\\
A_{s,1}&=&\sum_t2^{k+1-m}T(s\circ1\circ t)\end{eqnarray*}
These are the expected values of $T$ for a sequence which begins with $s$, has
either 0 or 1 as its $(k+1)$-st term, and continues randomly.  They
can be estimated by sampling.  Let $p_s$ be the
fraction of the seeds which give $s$ as the first $k$ bits which give
0 as the $(k+1)-st$ bit (thus $p_s=f_{s\circ0}/f_s$).
Then \begin{eqnarray}A_k(s)&=&\frac12A_{s,0}+
\frac12A_{s,1}\label{ado}
\\A_{k+1}(s)&=&p_sA_{s,0}+(1-p_s)A_{s,1}\label{but}\end{eqnarray}
If we could estimate $p_s$ from (\ref{but}), it would be simple to predict
the next generated bit after $s$.  Unfortunately, we cannot efficiently
estimate $A_{k+1}(s)$.  The problem is that we would have to sample 
among the seeds which generate $s$, and there is no easy way to find
such seeds.  Instead, we must find a way to use the information that
the average of $A_{k+1}(s)$ is $A_{k+1}$, which we can estimate.
\pq The (far from obvious) idea will be to have the prediction of the
$(k+1)$-st bit itself be random.  As we will see below, the probabilities
can be assigned to the two possible predictions can be chosen so that
the expected number of correct guesses looks like the right-hand side
of (\ref{but}).
\pq We will assume $A_{k+1}>A_k$ [remember, we chose $k$ so that the
difference between the two is significant]. The other case can be
handled similarly.  If $A_{s,0}>A_k(s)>A_{s,1}$, we would
expect sequences beginning with $s\circ0$ to look more like things
from the generator than sequences beginning with $s\circ1$.  Our
prediction for the next bit following $s$ will be random, given by
$${\renewcommand{\arraystretch}{1.2}
\hbox{Predict }\left\{{}\begin{tabular}{l}
0 with probability $\frac12+A_{s,0}-A_k(s)$\\
1 with probability $\frac12+A_{s,1}-A_k(s)$\end{tabular}\right.}$$
[The probabilities add to 1 by equation (\ref{ado}).]\pq
The probability that the prediction for input $s$ is correct is
\begin{eqnarray*}p_s\left(\frac12+A_{s,0}-A_k(s)\right)+(1-p_s)\left(
\frac12+A_{s,1}-A_k(s)\right)&=\\ \frac12+p_sA_{s,0}+(1-p_s)A_{s,1}
-A_k(s)&=&\frac12+A_{k+1}(s)-A_k(s)\end{eqnarray*}
When we average over all seeds resulting in all possible $s$, we get 
a correct prediction with probability $1/2+A_{k+1}-A_k$, which,
by (\ref{kch}), is significantly greater than $1/2$.
\footnote{Thanks to R.\ Sengupta for pointing out the importance of the
expression for $A_{k+1}(s)-A_k(s)$.}
\subsubsection{A consequence involving symmetry}
The Next Bit Condition was stated in a way that clearly distinguished
the beginning of the pseudo-random sequence from the end.  By contrast,
the Efficient Test Theorem treats a pseudo-random sequence in a completely
symmetrical way.  From that point of view, it does not matter which
end of the sequence is used to start the construction. This leads to
\begin{Co}Let $n=pq$.  Start with a random $1\le x\le n-1$ not divisible
by $p$ or $q$.  Let $a_0=x$, $\co{a_{i+1}}{a_i^2}n$.  The sequence of
bits given by $a_i$~mod~2 satisfies all efficient tests.\end{Co}
\section{Further results on pseudo-random generators}
The two main results of the preceding section were the Next Bit
Theorem and the Efficient Tests Theorem.  The former depended
on (QRA) and facts about squares and pseudo-squares.  The latter
was an abstract result about properties of arbitrary generators.
Our first result is an abstract version of the Next Bit Theorem.
\subsection{Hard-Core Predicates}
We have seen several functions $f$ with the property that $f(x)$
was easy to compute but $f^{-1}$ was difficult.  Such an $f$ is called
a {\it one-way function}. A {\it predicate\/}
is a property $B$ which is true or false for any $x$.
Typical examples might be ``is an odd number'' or ``is $\le50$.''
A {\it hard-core predicate\/} for a function $f$ is a predicate
such that\begin{enumerate}\item
 there is an efficient algorithm for deciding if $B(x)$ is 
true\item if we are given $f(x)$, there is no efficient algorithm
for guessing whether $B(x)$ is true which has a probability much
greater than $1/2$ of being right.\end{enumerate}
The example we used in the quadratic number generator was 
$$f(x)\equiv x^2\hbox{ mod }n\qquad B(x)=\hbox{ ``$x$ is odd''}$$
where we are looking only at those $x$ which are squares.
\begin{Th}If $B$ is a hard-core predicate for $f$, the random number
generator in which a seed $x$ is chosen at random, giving the 
sequence of bits$$B(x)\quad B(f^{-1}(x))\quad B(f^{-1}(f^{-1}(x)))
\dots$$satisfies the Next Bit Condition.\end{Th}As a corollary,
the sequence $B(x)$, $B(f(x))$,\dots satisfies all efficient tests,
using the arguments in the preceding section.
\pq{\bf Proof:} If there were a program which took as input a se%
quence of bits and could guess the next bit, we could get a good
guess on $B(x)$ with $f(x)$ known
by asking the next-bit predictor what would
occur next in the sequence$$B(f^{(k)}(x))\quad B(f^{(k-1)}(x)\dots
B(f(f(x)))\quad B(f(x))$$(this is really the same proof as 
in the case of the quadratic generator).
\pq It seems much harder to find examples of hard-core predicates
than of one-way functions $f$.  The latter can be obtained from
discrete logarithms, subset-sum problems, and elsewhere.
The difficulty is the problem of showing that a property cannot
be guessed with any significant probability.  This makes the
following result\footnote{Paper by Goldreich and Levin in 1989
{\it Symposium on Theory of Computation}}interesting.
\begin{Th}\label{HC}Let $f$ be a one-way function which maps sequences
of bits of length $k$ to sequences of length $k$. For each
$S\subset\{1,\dots k\}$ and sequence $x$ define
$B(S,x)$ to be true if the number of 1's in positions in $x$ specified
by $S$ is even.  There is no efficient algorithm which can guess
$B(S,x)$ given $S$ and $f(x)$ with probability much greater than
$1/2$.\end{Th}
Note this result does not rule out the possiblity that, for some
specific $S$, it may be possible to identify $B(S,x)$.  What is
being claimed is that, if $S$ is chosen at random, the chance
of this is small.
\pq I do not understand the proof myself, so will not attempt to
reproduce it here.
\subsection{A recent pseudo-random number generator}
The following simple generator was proposed in the 1989 {\it
Foundations of Computer Science}.\pq Choose $1\le a_i\le 2^k$
randomly, for $1\le i\le n$, with $n<k<1.5n$.  Choose $S\subset
\{1,\dots n\}$ randomly.  Add the $a_i$ corresponding to $S$
mod $2^k$ to obtain a sequence of $k$ bits.  Use the first $k-n$
bits as output from the generator.  The remaining $n$ bits give
you a new $S$, for which you obtain a new sum.  At each step,
you get $k-n$ random bits.\pq The authors of the paper 
(Impagliazzo and Naor) prove that this generator satisfies the
Next Bit Condition if the subset-sum problem cannot be solved
efficiently.  Their proof uses Theorem~\ref{HC}.
\end{document}


