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An Improved Lower Bound for the Quantum Query Complexity of Collision Finding in Hash Functions with Non-Uniformly Distributed Images

Marko Balogh

Published May 26, 2017• 0 citations
Publisher

Abstract

This work proves an improved lower bound for the quantum query complexity of collision- finding in hash functions whose images are distributed according to a non-uniform distribution. Recent work by [TTU16] applied the leftover hash lemma to show that at least Omega(2k/9) quantum queries are necessary to find a collision when the image distribution has min-entropy k. In comparison, a result by [Zha15] implies that Omega(2k/3) quantum queries are necessary if the distribution is uniform. In this paper the lower bound complexity of [Zha15] is extended directly to the non-uniform case using a chain of reductions which convert collisions in min-entropy k distributions into collisions in the uniform distribution with constant probability. This result shows a minimum security guarantee for hash functions under more general assumptions in which the image distribution may not be uniform.

DOI
10.15760/honors.421

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This work proves an improved lower bound for the quantum query complexity of collision- finding in hash functions whose images are distributed according to a non-uniform distribution. Recent work by [TTU16] applied the leftover hash lemma to show that at least Omega(2k/9) quantum queries are necessary to find a collision when the image distribution has min-entropy k. In comparison, a result by [Zha15] implies that Omega(2k/3) quantum queries are necessary if the distribution is uniform. In this paper the lower bound complexity of [Zha15] is extended directly to the non-uniform case using a chain of reductions which convert collisions in min-entropy k distributions into collisions in the uniform distribution with constant probability. This result shows a minimum security guarantee for hash functions under more general assumptions in which the image distribution may not be uniform.
Abstract Collision resistance is one of the most fundamental properties in cryptography. With the development of quantum computing, significant attention has been directed toward understanding the quantum query complexity of collision-finding problems in hash functions. The quantum query complexity of collision-finding in general non-uniform random functions remained an open problem until the recent work of Peng et al. (ASIACRYPT 2025), who nearly resolved it by introducing a novel parameter γ\gammaγ γ . Based on this parameter, they established an upper bound of O(γ1/6)O(\gamma ^{1/6})O(γ1/6) O ( γ 1 / 6 ) and a matching lower bound of Ω~(γ1/6)\tilde{\Omega }(\gamma ^{1/6})Ω~(γ1/6) Ω ~ ( γ 1 / 6 ) . However, the quantum query complexity of finding multi-collisions in non-uniform random functions has remained open. A s -collision to a function f is a set of distinct input x1,…,xsx_1,\dots , x_sx1​,…,xs​ x 1 , ⋯ , x s such that f(x1)=f(x2)⋯=f(xs)f(x_1)=f(x_2)\dots =f(x_s)f(x1​)=f(x2​)⋯=f(xs​) f ( x 1 ) = f ( x 2 ) ⋯ = f ( x s ) . In this work, we address this challenge by similarly introducing a new generalized parameter γs\gamma _sγs​ γ s . Based on this parameter, we establish nearly tight bounds—an upper bound of O(γs)O(\gamma _s)O(γs​) O ( γ s ) and a lower bound of Ω~(γs)\tilde{\Omega }(\gamma _s)Ω~(γs​) Ω ~ ( γ s ) —thereby nearly resolving this open problem. Moreover, although the existing results for 2-collisions do not generalize straightforwardly to the case of s -collision ( s>2s>2s>2 s > 2 ), we demonstrate that the result of Peng et al. can be viewed as a special case of our more general framework. This clearly establishes our work as a direct generalization of their contribution.
A k-collision for a compressing hash function H is a set of k distinct inputs that all map to the same output. In this work, we show that for any constant k, Θ(N12(1−12k−1))\varTheta \left( N^{\frac{1}{2}(1-\frac{1}{2^k-1})}\right) Θ(N21​(1−2k−11​)) quantum queries are both necessary and sufficient to achieve a k-collision with constant probability. This improves on both the best prior upper bound (Hosoyamada et al., ASIACRYPT 2017) and provides the first non-trivial lower bound, completely resolving the problem.
A kkk-collision for a compressing hash function HHH is a set of kkk distinct inputs that all map to the same output. In this work, we show that for any constant kkk, Θ(N12(1−12k−1))Θ\left(N^{\frac{1}{2}(1-\frac{1}{2^k-1})}\right)Θ(N21​(1−2k−11​)) quantum queries are both necessary and sufficient to achieve a kkk-collision with constant probability. This improves on both the best prior upper bound (Hosoyamada et al., ASIACRYPT 2017) and provides the first non-trivial lower bound, completely resolving the problem.
Extraction of uniform randomness from (noisy) non-uniform sources is an important primitive in many security applications, e.g. (pseudo-)random number generators, privacy-preserving biometrics, and key storage based on Physical Unclonable Functions. Generic extraction methods exist, using universal hash functions. There is a trade-off between the length of the extracted bit string and the uniformity of the string. In the literature there are proven lower bounds on this length as a function of the desired uniformity. The best known bound involves a quantity known as smooth min-entropy. Unfortunately, there exist at least three definitions of smooth entropy. In this paper we compare three of these definitions, and we derive improved lower bounds on the extractable randomness.\nWe also investigate the use of almost universal hash functions, which are slightly worse at extracting randomness than universal hash functions, but are preferable in practice because they require far less resources in devices. We show that using them has negligible effect on the extractable randomness.\nKeywords: Leftover Hash Lemma; Universal hash function; Randomness extraction