Cryptology ePrint Archive: Report 2021/637

Doubly-Affine Extractors, and their Applications

Yevgeniy Dodis and Kevin Yeo

Abstract: In this work we challenge the common misconception that information-theoretic (IT) privacy is too impractical to be used in the real-world: we propose to build simple and $\textit{reusable}$ IT-encryption solutions whose only efficiency penalty (compared to computationally-secure schemes) comes from a large secret key size, which is often a rather minor inconvenience, as storage is cheap. In particular, our solutions are $\textit{stateless}$ and $\textit{locally computable at the optimal rate}$, meaning that honest parties do not maintain state and read only (optimally) small portions of their large keys with every use.

Moreover, we also propose a novel architecture for outsourcing the storage of these long keys to a network of semi-trusted servers, trading the need to store large secrets with the assumption that it is hard to simultaneously compromise too many publicly accessible ad-hoc servers. Our architecture supports $\textit{everlasting privacy}$ and $\textit{post-application security}$ of the derived one-time keys, resolving two major limitations of a related model for outsourcing key storage, called bounded storage model.

Both of these results come from nearly optimal constructions of so called $\textit{doubly-affine extractors}$: locally-computable, seeded extractors $\textbf{Ext}$(X,S) which are linear functions of X (for any fixed seed S), and protect against bounded affine leakage on X. This holds unconditionally, even if (a) affine leakage may $\textit{adaptively depend}$ on the extracted key R = $\textbf{Ext}$(X, S); and (b) the seed S is only $\textit{computationally}$ secure. Neither of properties are possible with general-leakage extractors.

Category / Keywords: foundations / extractors, information-theoretic privacy, everlasting privacy

Original Publication (with major differences): ITC 2021

Date: received 14 May 2021

Contact author: dodis at cs nyu edu,kwlyeo@google com

Available format(s): PDF | BibTeX Citation

Version: 20210517:063736 (All versions of this report)

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