Metraipolis › archive › dc_0ccabcbe6cb4

Fast Simulation Algorithms for OLH using Binomial Modeling — arXiv:2608.15778

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.15778
License (as archived): CC0-1.0
Archived: 2026-10-18T19:33:56.285Z
SHA-256 of the archived text: 1793fbaa7db558431e15ce05bf2701d741836fabda6e4d327d43828043057657
Stable URL of this page: https://metraipolis.com/documento/dc_0ccabcbe6cb4

How to cite

Fast Simulation Algorithms for OLH using Binomial Modeling — arXiv:2608.15778 — arXiv (en), https://arxiv.org/abs/2608.15778 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0ccabcbe6cb4 (Metraipolis archive dc_0ccabcbe6cb4, sha256 1793fbaa7db558431e15ce05bf2701d741836fabda6e4d327d43828043057657)

BibTeX

@misc{dc0ccabcbe6cb4,
  title = {Fast Simulation Algorithms for OLH using Binomial Modeling — arXiv:2608.15778},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.15778},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0ccabcbe6cb4; sha256 1793fbaa7db558431e15ce05bf2701d741836fabda6e4d327d43828043057657},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.15778. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Optimized Local Hashing (OLH) is a widely used hash-based Local Differential Privacy (LDP) protocol, and simulation-based experimentation is the standard approach for evaluating OLH and OLH-based applications in research. However, the existing OLH simulations have $O(nd)$ computational complexity, where $n$ is the user population size and $d$ is the domain size, and can lead to significant execution times as $n$ and $d$ grow. In this paper, we propose two fast simulation algorithms for OLH (2-Binom and 3-Binom) grounded in Binomial modeling. Our key insight is that, for any domain value $v$, the total number of users whose perturbed reports support $v$ can be decomposed into a sum of two or three Binomial random variables. Using this insight, our algorithms reduce the simulation complexity to $O(n + d)$ without hurting statistical equivalence. In particular, we theoretically prove that both algorithms yield unbiased frequency estimations with variances identical to those of the original OLH simulations. Experiments on real-world datasets confirm that both approaches reduce execution times from several minutes to milliseconds, yielding significant speedups with no change in utility.

Tema degli esploratori: fiducia digitale e crittografia (retro-catalogo arXiv: annunci del 2026-08-18 in cs.CR).
Categorie arXiv: Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2608.15778 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T19:01:59.118Z dalla pagina https://arxiv.org/catchup/cs.CR/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).