Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries
A randomness-efficient analogue of the norm mechanism for differentially private linear queries reduces the random-bit budget, potentially making privacy-preserving analytics more practical for constrained environments.
Summary written by editorial AI · Source link below
arXiv:2609.02880v1 Announce Type: new 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 $\| \cdot \|_K$-norm mechanism of Hardt and Talwar [HT10]. For the $\ell_\infty$-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 $\
Editorial Analysis
Lower computational overhead for differential privacy mechanisms could accelerate adoption of privacy-preserving analytics in GDPR-regulated European enterprises.
Track maturation of randomness-efficient DP mechanisms for potential integration into your privacy-preserving analytics stack.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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