Collaborative, Privacy-Preserving Data Aggregation at Scale

Combining and analyzing data collected at multiple locations is critical for a wide variety of applications, such as detecting and diagnosing malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data-analysis systems. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving collaboration among a large number of participants. Scalability is achieved through a “semi-centralized” architecture that divides responsibility between a proxy that obliviously blinds the client inputs and a database that identifies the (blinded) keywords that have values satisfying some evaluation function. Our solution leverages a novel cryptographic protocol that provably protects the privacy of both the participants and the keywords. For example, if web servers collaborate to detect source IP addresses responsible for denial-of-service attacks, our pr...

• Type: Conference
• Year: 2010
• Where: PET
• Authors: Benny Applebaum, Haakon Ringberg, Michael J. Freedman, Matthew Caesar, Jennifer Rexford
• Keywords: Collaborative Data-analysis Systems, Denial-of-service Attacks, IP Addresses, PET 2010, Security Privacy