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arXiv · 2603.15023

SIMD-PAC-DB: Pretty Performant PAC Privacy

Ilaria Battiston·Dandan Yuan·Xiaochen Zhu·Peter Boncz
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Abstract

This work presents a highly optimized implementation of PAC-DB, a recent and promising database privacy model. We prove that our SIMD-PAC-DB can compute the same privatized answer with just a single query, instead of the 128 stochastic executions against different 50% database sub-samples needed by the original PAC-DB. Our key insight is that every bit of a hashed primary key can be seen to represent membership of such a sub-sample. We present new algorithms for approximate computation of stochastic aggregates based on these hashes, which, thanks to their SIMD-friendliness, run up to 40x faster than scalar equivalents. We release an open-source DuckDB community extension which includes a rewriter that PAC-privatizes arbitrary SQL queries. Our experiments on TPC-H, Clickbench, and SQLStorm evaluate thousands of queries in terms of performance and utility, significantly advancing the ease of use and functionality of privacy-aware data systems in practice.

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Ilaria Battiston, Dandan Yuan, Xiaochen Zhu, Peter Boncz. 2026-03-16. SIMD-PAC-DB: Pretty Performant PAC Privacy. https://arxiv.org/abs/2603.15023

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