Thrifty privacy: efficient support for privacy-preserving publish/subscribe

  • Authors:

  • Raphaël Barazzutti;Pascal Felber;Hugues Mercier;Emanuel Onica;Etienne Rivière

  • Affiliations:

  • University of Neuchâtel, Switzerland;University of Neuchâtel, Switzerland;University of Neuchâtel, Switzerland;University of Neuchâtel, Switzerland;University of Neuchâtel, Switzerland

  • Venue:
  • Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
  • Year:
  • 2012

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Abstract

Content-based publish/subscribe is an appealing paradigm for building large-scale distributed applications. Such applications are often deployed over multiple administrative domains, some of which may not be trusted. Recent attacks in public clouds indicate that a major concern in untrusted domains is the enforcement of privacy. By routing data based on subscriptions evaluated on the content of publications, publish/subscribe systems can expose critical information to unauthorized parties. Information leakage can be avoided by the means of privacy-preserving filtering, which is supported by several mechanisms for encrypted matching. Unfortunately, all existing approaches have in common a high performance overhead and the difficulty to use classical optimization for content-based filtering such as per-attribute containment. In this paper, we propose a novel mechanism that greatly reduces the cost of supporting privacy-preserving filtering based on encrypted matching operators. It is based on a pre-filtering stage that can be combined with containment graphs, if available. Our experiments indicate that pre-filtering is able to significantly reduce the number of encrypted matching for a variety of workloads, and therefore the costs associated with the cryptographic mechanisms. Furthermore, our analysis shows that the additional data structures used for pre-filtering have very limited impact on the effectiveness of privacy preservation.