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When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery
Bo Jiang, Wanrong Zhang, Donghang Lu +2
Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols ha…
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach
Bo Jiang, Wanrong Zhang, Donghang Lu +3
Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under str…
Differentially Private Learned Indexes
Jianzhang Du, Tilak Mudgal, Rutvi Rahul Gadre +2
In this paper, we address the problem of efficiently answering predicate queries on encrypted databases, those secured by Trusted Execution Environments (TEEs), which enable untrus…
Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy
Yingtai Xiao, Jian Du, Shikun Zhang +4
Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desi…
Budget Recycling Differential Privacy
Bo Jiang, Jian Du, Sagar Sharma +1
Differential Privacy (DP) mechanisms usually {force} reduction in data utility by producing "out-of-bound" noisy results for a tight privacy budget. We introduce the Budget Recycli…