Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries

Abstract: We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the $| \cdot |<em>K$-norm mechanism of Hardt and Talwar [HT10]. For the $\ell</em>\infty$-error, our algorithm can answer $d$ linear queries with $O(d / \varepsilon)$ error using $O(\log d)$ random bits, improving upon algorithms of Canonne et al. and Ghentiyala [CSV25, Ghe26]; this is optimal when $\varepsilon \le 1/d$. We also provide a computationally efficient version of our algorithm, albeit with an $O(\log d)$ multiplicative increase in the error.