Distributional Collision Resistance Beyond One-Way Functions
Nir Bitansky, Iftach Haitner, Ilan Komargodski, Eylon Yogev

TL;DR
This paper introduces a new cryptographic notion called distributional collision resistance, explores its theoretical properties, and constructs a novel constant-round statistically hiding commitment scheme based on this concept.
Contribution
It establishes a connection between distributional collision resistance and statistically hiding commitments, providing new constructions and insights into their equivalence.
Findings
Constructs constant-round statistically hiding commitments from distributional collision resistance.
Shows two-message statistically hiding commitments imply distributional collision resistance.
Links average-case hardness in SZK to constant-round statistically hiding commitments.
Abstract
Distributional collision resistance is a relaxation of collision resistance that only requires that it is hard to sample a collision where is uniformly random and is uniformly random conditioned on colliding with . The notion lies between one-wayness and collision resistance, but its exact power is still not well-understood. On one hand, distributional collision resistant hash functions cannot be built from one-way functions in a black-box way, which may suggest that they are stronger. On the other hand, so far, they have not yielded any applications beyond one-way functions. Assuming distributional collision resistant hash functions, we construct \emph{constant-round} statistically hiding commitment scheme. Such commitments are not known based on one-way functions and are impossible to obtain from one-way functions in a black-box way. Our construction relies on the…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning · Cryptography and Data Security
