Privacy Preserving Selective Aggregation of Online User Behaviour Data
Tons of online user behaviour data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behaviour data to extract valuable information for research purposes or business interests. However, online users’ privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behaviour analysis. In this paper, we propose a scheme PPSA, which encrypts users’ sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behaviour analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behaviour dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.
Reference IEEE paper :
“Privacy-Preserving Selective Aggregation of Online User Behaviour Data” , IEEE Transactions on Computers, 2017.
Unique ID – SBI1070
Domain – SECURE COMPUTING