Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing

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.

@article{zhao2025_2503.17844,
  title={ Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing },
  author={ Dongfang Zhao },
  journal={arXiv preprint arXiv:2503.17844},
  year={ 2025 }
}