# Rafail Ostrovsky - Publications

Privacy Amplification with Asymptotically Optimal Entropy Loss

Nishanth Chandran, Bhavana Kanukurthi, Rafail Ostrovsky, Leonid Reyzin

Abstract:

We study the problem of privacy amplification'': key agreement between two parties who both know a weak secret $w$, such as a password. (Such a setting is ubiquitous on the internet, where passwords are the most commonly used security device.) We assume that the key agreement protocol is taking place in the presence of an active computationally unbounded adversary Eve. The adversary may have partial knowledge about $w$, so we assume only that $w$ has some entropy from Eve's point of view. Thus, the goal of the protocol is to convert this non-uniform secret $w$ into a uniformly distributed string $R$ that is fully secret from Eve. $R$ may then be used as a key for running symmetric cryptographic protocols (such as encryption, authentication, etc.).

Because we make no computational assumptions, the entropy in $R$ can come only from $w$. Thus such a protocol must minimize the entropy loss during its execution, so that $R$ is as long as possible. The best previous results have entropy loss of $\Theta(\kappa^2),$ where $\kappa$ is the security parameter, thus requiring the password to be very long even for small values of $\kappa$. In this work, we present the first protocol for information-theoretic key agreement that has entropy loss \textbf{linear} in the security parameter. The result is optimal up to constant factors. We achieve our improvement through a somewhat surprising application of error-correcting codes for the edit distance.

comment: Preliminary version appeared in STOC 2010. Full version appeared in IEEE Trans. Information Theory 60(4): 2373-2390 (2014)

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