Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing
Computational Complexity
2025-03-25 v1 Cryptography and Security
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.
Keywords
Cite
@article{arxiv.2503.17844,
title = {Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing},
author = {Dongfang Zhao},
journal= {arXiv preprint arXiv:2503.17844},
year = {2025}
}