Private Count Release: A Simple and Scalable Approach for Private Data Analytics

arXiv:2403.05073v1 Announce Type: new
Abstract: We present a data analytics system that ensures accurate counts can be released with differential privacy and minimal onboarding effort while showing instances that outperform other approaches that require more onboarding effort. The primary difference between our proposal and existing approaches is that it does not rely on user contribution bounds over distinct elements, i.e. $\ell_0$-sensitivity bounds, which can significantly bias counts. Contribution bounds for $\ell_0$-sensitivity have been considered as necessary to ensure differential privacy, …

analytics arxiv can cs.cr data data analytics differential privacy onboarding privacy private private data release simple system