Thrifty Privacy: Efficient Support for Privacy-Preserving Publish/Subscribe
| Résumé |
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. |
| Mots-clés |
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| Citation | R. Barazzutti, et al., "Thrifty Privacy: Efficient Support for Privacy-Preserving Publish/Subscribe," in Proceedings of the International Conference on Distributed Event-Based Systems (DEBS'12), Berlin, Germany, 2012. |
| Type | Actes de congrès (Anglais) |
| Nom de la conférence | Proceedings of the International Conference on Distributed Event-Based Systems (DEBS'12) (Berlin, Germany) |
| Date de la conférence | 13-1-2012 |
| Editeur commercial | ACM |
| URL | http://dx.doi.org/10.1145/2335484.2335509 |
| Liée au projet | SRT-15: Intelligence Push in the Enterprise Realm |