Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing

Dongfang Zhao

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arXiv · 2503.17844

Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing

Dongfang Zhao

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Abstract

We study the problem of approximating Hamming distance in sublinear time under property-preserving hashing (PPH), where only hashed representations of inputs are available. Building on the threshold evaluation framework of Fleischhacker, Larsen, and Simkin (EUROCRYPT 2022), we present a sequence of constructions with progressively improved complexity: a baseline binary search algorithm, a refined variant with constant repetition per query, and a novel hash design that enables constant-time approximation without oracle access. Our results demonstrate that approximate distance recovery is possible under strong cryptographic guarantees, bridging efficiency and security in similarity estimation.

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Dongfang Zhao. 2025-03-22. Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing. https://arxiv.org/abs/2503.17844

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