5 papers
Privacy Filters are Captured by Residues: A Characterization of Free Natural Filters and the Cost of Adaptivity
Matthew Regehr, Bingshan Hu, Ethan Leeman +3
We study privacy filters, which enable privacy accounting for differentially private (DP) mechanisms with adaptively chosen privacy characteristics. We develop a general theory tha…
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
Bingshan Hu, Zhiming Huang, Tianyue H. Zhang +2
We address differentially private stochastic bandit problems from the angles of exploring the deep connections among Thompson Sampling with Gaussian priors, Gaussian mechanisms, an…
Big Bird: Resilient Privacy Budgeting Across Untrusted Web Domains
Pierre Tholoniat, Alison Caulfield, Giorgio Cavicchioli +6
The W3C Attribution API is an emerging standard for privacy-preserving advertising measurement. Its current privacy architecture enforces individual differential privacy (IDP) inde…
Efficient and Adaptive Posterior Sampling Algorithms for Bandits
Bingshan Hu, Zhiming Huang, Tianyue H. Zhang +2
We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors […
NetShaper: A Differentially Private Network Side-Channel Mitigation System
Amir Sabzi, Rut Vora, Swati Goswami +3
The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet applications. However, these protocols cannot prevent ne…