Collaborative, privacy-preserving data aggregation at scale

Benny Applebaum, Haakon Ringberg, Michael Joseph Freedman, Matthew Caesar, Jennifer L. Rexford

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


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. Our prototype implementation can handle over a million suspect IP addresses per hour when deployed across only two quad-core servers, and its throughput scales linearly with additional computational resources.

Original languageEnglish (US)
Title of host publicationPrivacy Enhancing Technologies - 10th International Symposium, PETS 2010, Proceedings
Number of pages19
StatePublished - 2010
Event10th International Symposium on Privacy Enhancing Technologies, PETS 2010 - Berlin, Germany
Duration: Jul 21 2010Jul 23 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6205 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Symposium on Privacy Enhancing Technologies, PETS 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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