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\lecture{21}{November 27, 2002}{Madhu Sudan}{Deniss \v{C}ebikins}
\section{Applications of error-correcting codes}
Since the main purpose of computers is to manipulate information, it
is clear that every computer must have some information preserving
capabilities. The original motivation for studying error-correcting
was to develop methods of protecting data from occasional transmission
errors. Recent progress in coding theory revealed quite fascinating
applications of these codes to other areas in computer science and
mathematics. In this lecture, we take a look at how certain
combinatorial properties of error-correcting codes can be used in
solving seemingly unrelated problems.
\section{Communication complexity}
Suppose that Alice has a string $x\in\{0,1\}^k$ and Bob has a string
$y\in\{0,1\}^k$. They want to find the value of $f(x,y)$ for some
boolean function $f : \{0,1\}^k\times \{0,1\}^k \rightarrow \{0,1\}$
which is known to both of them. For example, if they want to determine
whether their strings $x$ and $y$ are equal, then $f$ is the function
$EQ$ defined by
$$
EQ(x,y) = \left\{\begin{array}{ll}
1, &\mbox{if $x=y$,} \\
0, &\mbox{if $x\neq y$.}
\end{array}\right.
$$
Alice and Bob also want to minimize
the number of bits they need to send to each
other in order to compute $f(x,y)$. For now, assume that the
interaction occurs according to a certain deterministic
procedure, which we call a
\textit{protocol}, in which each message that Alice (resp. Bob)
transmits depends only on $x$ (resp. $y$) and the previous
messages. More formally, a protocol can be described as follows:
\vskip5pt
- First, Alice computes $a_1\leftarrow A_1(x)$ and sends it to Bob;
\vskip5pt
- Bob computes $b_1 \leftarrow B_1(y,a_1)$ and sends it back to Alice;
\vskip5pt
- Alice computes $a_2\leftarrow A_2(x,a_1,b_1)$ and sends it to Bob;
\vskip5pt
- Bob computes $b_2\leftarrow B_2(y,a_1,b_1,a_2)$ and sends it to
Alice;
\vskip5pt
- Etc.
Here $A_1$, $A_2$, \dots, and $B_1$, $B_2$, \dots, are sequences of
deterministic algorithms modeling Alice's and Bob's behavior,
respectively. We require that the lengths $|a_i|$ and $|b_i|$ of
messages transmitted at each step depend only on the protocol $P$.
We also require that once a person determines $f(x,y)$,
he or she sends it to the other person at the last step of the protocol.
For a fixed $k$, let the \textit{size} of a protocol $P$ be the
maximum total
number of bits, over all possible pairs $x,y\in\{0,1\}^k$,
that Alice and Bob transmit during the execution of the protocol. We
define the \textit{communication complexity} of a boolean fuction $f$,
denoted $\CC(f)$,
to be the smallest size of a protocol at the end of which both parties
know $f(x,y)$.
An easy way to find $f(x,y)$ in this scenario is to have Alice send to
Bob the entire string $x$, and then have Bob compute $f(x,y)$ and send
the obtained value back to Alice. In this protocol, a total of $k+1$
bits is transmitted. Thus, we get a bound
$$
\CC(f)\leq k+1
$$
valid for all boolean functions $f$.
It is natural to ask if the above bound is tight. That is, is there a
function $f$ whose communication complexity is exactly $k+1$? The
answer is ``yes''; moreover, the equality function $EQ(x,y)$ has this property.
\vskip11pt
\begin{proposition} $\CC(EQ) = k+1$.
\end{proposition}
\begin{proof} Suppose some protocol $P = (A_1,B_1,A_2,B_2,\dots)$ of size at most $k$ determines
$EQ(x,y)$. Then the interaction history $(a_1,b_1,a_2,b_2,\dots)$
before one of the parties determines $EQ(x,y)$ can take at most
$2^{k-1}$ values. Since there are $2^k$ strings in $\{0,1\}^k$,
there exist distinct strings $x,x'\in\{0,1\}^k$ such that running
$P$ on inputs $(x,x)$ and $(x',x')$ produces the same interaction
history $(a_1,b_1,\dots)$. Consider the interaction history
$(a_1', b_1', \dots)$ resulting from running $P$
on input $(x,x')$. We get
$$
a_1' = A_1(x) = a_1
$$
$$
b_1' = B_1(x', a_1') = B_1(x', a_1) = b_1
$$
$$
a_2' = A_2(x, a_1', b_1') = A_2(x, a_1, b_1) = a_2
$$
$$
\dots
$$
Thus running $P$ on $(x, x')$ produces the same interaction history
as running $P$ on $(x,x)$, so we must have $EQ(x,x') = EQ(x,x)$, which
is a contradiction. We conclude that $\CC(EQ) = k+1$.
\end{proof}
\section{Probabilistic communication complexity}
Let us modify the scenario of the previous section by allowing Alice
and Bob to generate random bits in private. That is, the parties are
permitted to use random bits in producing messages, but they are not
allowed to share these random bits with the other party.
This modification is useful only if we make our requirement of
Alice and Bob determining $f(x,y)$ somewhat less restrictive. For this
reason, let us accept the following in this scenario: a
probabilistic protocol described above determines the function $f$ if
for any $x,y\in\{0,1\}^k$, the probability that the parties compute
$f(x,y)$ at the last step correctly is at least $2/3$. We define the
\textit{probabilistic communication complexity} of a boolean function
$f$, denoted $\PCC(f)$,
to be the size of a smallest probabilistic protocol that
determines $f$.
It turns out that probabilistic communication complexity of a boolean
function can be significantly smaller than its ``regular''
communication complexity. Again, we consider the example of the
equality function $EQ(x,y)$.
Consider the following probabilistic protocol:
\vskip5pt
- Alice chooses an index $i\in\{1,2,\dots,k\}$ at random and sends
$(i,x_i)$ to Bob (here $x_i$ denotes the $i$-th bit of $x$);
\vskip5pt
- Bob sends $1$ to Alice if $x_i = y_i$, and $0$ otherwise.
\vskip5pt
Clearly, if $x=y$, then Bob always sends the correct value of
$EQ(x,y)$ to Alice at the last step. If $x\neq y$, then Bob sends the
correct value of $EQ(x,y)$ if $x$ and $y$ differ in the $i$-th bit,
which is true with probability of at least $1/k$.
This is not quite meeting our goal, but
we can use error-correcting codes to enhance the above construction
and
design
small size probabilistic protocols determining $EQ$.
\begin{proposition}
$\PCC(EQ) = O(\log k)$.
\end{proposition}
\begin{proof}
Let $E : \{0,1\}^k \rightarrow \FF_q^{3k}$ be the encoding function of
a Reed-Solomon $[3k,k,2k]_q$-code, where $q$ is slightly greater than
$3k$. Consider the following probabilistic protocol:
\vskip5pt
- Alice chooses an index $i\in\{1,2,\dots,3k\}$ at random and sends
$(i,E(x)_i)$
to
Bob, where $E(x)_i$ denotes the $i$-th coordinate of $E(x)$ (we regard
$\FF^{3k}_q$ as a $3k$-dimensional vector space over $\FF_q$);
\vskip5pt
- Bob sends $1$ to Alice if $E(x)_i = E(y)_i$, and $0$ otherwise.
\vskip5pt
If $x=y$, then Bob always sends the correct value of $EQ(x,y)$ to
Alice at the last step. If $x\neq y$, then $E(x)$ and $E(y)$ differ in
at least $2k$ out of $3k$ coordinates, so the probability that $E(x)$
and $E(y)$ differ in the $i$-th coordinate and hence Bob sends the
correct value of $EQ(x,y)$ to Alice is at least $2/3$, as desired.
It remains to compute the number of bits transmitted in the above
protocol. We need $\log 3k$ bits to transmit $i$, and since $E(x)_i
\in \FF_q$, we need $\log q \approx \log 3k$ bits to transmit
$E(x)_i$. Finally, Bob transmits one more bit at the last step, so
$$
\PCC(EQ) \leq \log 3k + \log q + 1 = O(\log k).
$$
\end{proof}
We conclude the section by considering the scenario in which Alice and
Bob share their random bits. In this case, Alice and Bob are first
given the function $f$. Then, they generate a random string $s$
together. Afterwards, Alice is given $x$ and Bob is given $y$, and
they determine $f(x,y)$ according to a deterministic protocol in which
they are allowed to use their knowledge of $s$.
It is clear that in this scenario, Alice and Bob can determine
$EQ(x,y)$ by transmitting $O(\log k)$ bits using the protocol of the
above proposition; the only difference is that the index $i$ is
generated by the two parties together rather than by Alice alone. We
can also increase the efficiency of the protocol since the index $i$
does not need to be transmitted. If we choose a binary code with
relative minimum distance of at least $2/3$, then Alice and Bob need to
transmit only two bits, namely, $E(x)_i$ and the bit sent by Bob at
the last step.
\section{Secret sharing}
In this section, we discuss
an application of error-correcting codes
to the problem of secret sharing. Suppose we have a piece $s$
of secret information, and we want to ``spread'' this information
across $n$ parties, $p_1$, \dots, $p_n$.
We do so by generating a random string $r$ and letting the party $p_i$
know the value of $s_i=f_i(s,r)$, where $f_i$ is a function which is
up to us to define. We want our secret sharing scheme to satisfy two
basic properties:
\vskip5pt
- Secrecy: no subset $\{p_{i_1},\dots,p_{i_t}\}$ of $t$ parties can
determine $s$ from their shares $s_{i_1}$, \dots, $s_{i_t}$ of the
secret;
\vskip5pt
- Recovery: every subset $\{p_{i_1},\dots,p_{i_T}\}$ of $T$ parties
can determine $s$ from their shares $s_{i_1}$, \dots, $s_{i_T}$ of the
secret.
\vskip5pt
Here $1\leq t < T \leq n$ are fixed parameters. The following
construction due to Shamir achieves the desired properties in the
``ideal'' case $T=t+1$. The secret information $s$ in this
construction is an element of the field $\FF_q$.
\vskip5pt
- Given $s$, choose non-zero elements $\alpha_1$, \dots, $\alpha_t$ of
$\FF_q$ and distinct elements $\beta_1$, \dots, $\beta_n$ of $\FF_q$;
\vskip5pt
- Set $p(x) = \alpha_t x^t + \dots + \alpha_1 x + s$ and $s_i =
(\beta_i, p(\beta_i))$.
\vskip5pt
We omit the proof of the validitiy of this scheme. Instead, we
consider another scheme, which is also constructed using an
error-correcting code.
Let $C$ be a linear $[n+1,k,d]_q$-code. Consider the following secret
sharing scheme ($s$ is a member of the alphabet of $C$):
\vskip5pt
- Choose a codeword $(c_0,c_1,\dots,c_n)\in C$ such that $c_o=s$;
\vskip5pt
- Set $s_i = c_i$.
\vskip5pt
Any $T$ parties can recover $s$ from their shares of the secret if it
is possible to decode a codeword with $n+1-T$ erasures (one erasure at
$c_0$, and $n-T$ more erasures corresponding the other $n-T$
parties). Such decoding is possible if $n-T+1 < d$.
It can also be shown that if the code $C^\perp$ dual to $C$
has minimum distance $\Delta$, then no subset of $\Delta-2$ parties
can determine $s$ from their shares of the secret.
Thus, if $n-T+1 \leq d-1$ and $t\leq \Delta-2$, then the above secret
sharing scheme has the desired properties. To show that the two
conditions can be satisfied in the ``ideal'' case $T=t+1$, let $C$ be
a Reed-Solomon $[n+1, t+1, n-t+1]_q$-code. Then its dual is an $[n+1,
n-t, t+2]$-code. Thus, $\Delta = t+2$ and $d = n-t+1 = n-T+2$, so
the above conditions are satisfied.
\end{document}