A Differentially Private Selective Aggregation Scheme for Online User Behavior Analysis

Jianwei Qian, Fudong Qiu, Fan Wu, Na Ruan, Guihai Chen, Shaojie Tang

Abstract: Online user behavior analysis is becoming increasingly important, and offers valuable information to analysts for developing better e-commerce strategies. However, it also raises significant privacy concerns. Recently, growing efforts have been devoted to protecting the privacy of individuals while data aggregation is performed, which is a critical operation in behavior analysis. Unfortunately, existing methods allow very limited aggregation over user data, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users' sensitive data to prevent privacy leakage from both analysts and the aggregation service provider, and fully supports selective aggregate functions for differentially private data analysis. We have implemented our design and evaluated its performance using a trace-driven evaluation based on an online behavior dataset. Evaluation results show that our scheme effectively supports various selective aggregate queries with acceptable computation and communication overheads.

External IDs:dblp:conf/globecom/QianQWRCT15