Cryptology ePrint Archive: Report 2018/480

On Distributional Collision Resistant Hashing

Ilan Komargodski and Eylon Yogev

Abstract: Collision resistant hashing is a fundamental concept that is the basis for many of the important cryptographic primitives and protocols. Collision resistant hashing is a family of compressing functions such that no efficient adversary can find any collision given a random function in the family.

In this work we study a relaxation of collision resistance called distributional collision resistance, introduced by Dubrov and Ishai (STOC '06). This relaxation of collision resistance only guarantees that no efficient adversary, given a random function in the family, can sample a pair $(x,y)$ where $x$ is uniformly random and $y$ is uniformly random conditioned on colliding with $x$. Our first result shows that distributional collision resistance can be based on the existence of multi-collision resistance hash (with no additional assumptions). Multi-collision resistance is another relaxation of collision resistance which guarantees that an efficient adversary cannot find any tuple of $k>2$ inputs that collide relative to a random function in the family. The construction is non-explicit, non-black-box, and yields an infinitely-often secure family. This partially resolves a question of Berman et al. (EUROCRYPT '18). We further observe that in a black-box model such an implication (from multi-collision resistance to distributional collision resistance) does not exist.

Our second result is a construction of a distributional collision resistant hash from the average-case hardness of SZK. Previously, this assumption was not known to imply any form of collision resistance (other than the ones implied by one-way functions).

Category / Keywords: foundations / Collision Resistant Hashing, Distributional Hashing, Multi-Collision Resistance, Statistical Zero-Knowledge, Non-Black-Box Construction

Original Publication (in the same form): IACR-CRYPTO-2018

Date: received 20 May 2018, last revised 3 Sep 2018

Contact author: komargodski at cornell edu, eylon yogev@weizmann ac il

Available format(s): PDF | BibTeX Citation

Version: 20180903:194229 (All versions of this report)

Short URL: ia.cr/2018/480


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