Collaborative, Privacy-Preserving Data Aggregation at Scale

Combining and analyzing data collected at multiple administrative locations is critical for a wide variety of applications, such as detecting malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data aggregation. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving data aggregation (PDA) among a large number of participants. Scalability and efficiency is achieved through a “semi-centralized” architecture that divides responsibility between a proxy that obliviously blinds the client inputs and a database that aggregates values by (blinded) keywords and identifies those keywords whose values satisfy some evaluation function. Our solution leverages a novel cryptographic protocol that provably protects the privacy of both the participants and the keywords, provided that proxy and database do not collude, even if both parties may be individually malicious. We implemented a prototype of our design, including an amortized oblivious transfer protocol that substantially improves the efficiency of client-proxy interactions. Our experiments show that the performance of our system scales linearly with computing resources, making it easy to improve performance by adding more cores or machines. For collaborative diagnosis of denialof-service attacks, our system can handle millions of suspect IP addresses per hour when the proxy and the database each run on two quad-core machines.