# SIMD-PAC-DB: Pretty Performant PAC Privacy

arXiv: https://arxiv.org/abs/2603.15023 · HTML: https://mlsanity.com/paper/2603.15023/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization](https://mlsanity.com/paper/2605.06505/) (2026)
- [Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models](https://mlsanity.com/paper/2610.04038/) (2026)

## Abstract (arXiv)

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.