On Differentially Private Counting on Trees
Ghazi, Badih, Pritish Kamath, Kumar, Ravi, Pasin Manurangsi, Kewen Wu · arXiv (Cornell University) · 2022
We study the problem of performing counting queries at different levels in hierarchical structures while preserving individuals' privacy. Motivated by applications, we propose a new error measure for this problem by considering a combination of multiplicative and additive approximation to the query results. We examine known mechanisms in differential privacy (DP) and prove their optimality, under this measure, in the pure-DP setting. In the approximate-DP setting, we design new algorithms achieving significant improvements over known ones.