Problem
Research questions and friction points this paper is trying to address.
differential privacy
linear queries
random bits
efficiency
Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries
๐ค AI Summary
็ ็ฉถไฝฟ็จๅฐ้้ๆบไฝๅ็ญๅทฎๅ้็ง็บฟๆงๆฅ่ฏข็้ฎ้ข๏ผๆๅบไบไธ็งๆฐ็ฎๆณ๏ผๅจไฟ่ฏ่ฏฏๅทฎ็ๅๆถๅๅฐไบๆ้็้ๆบไฝๆฐใ
๐ 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 $\varepsilon \le 1/d$. We also provide a computationally efficient version of our algorithm, albeit with an $O(\log d)$ multiplicative increase in the error.
Innovation
Methods, ideas, or system contributions that make the work stand out.
randomness-efficient
differential privacy
linear queries
log d random bits
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