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Abstract: Secure multiparty computation MPC allows joint privacy-preservingcomputations on data of multiple parties. Although MPC has been studiedsubstantially, building solutions that are practical in terms of computationand communication cost is still a major challenge. In this paper, weinvestigate the practical usefulness of MPC for multi-domain network securityand monitoring. We first optimize MPC comparison operations for processing highvolume data in near real-time. We then design privacy-preserving protocols forevent correlation and aggregation of network traffic statistics, such asaddition of volume metrics, computation of feature entropy, and distinct itemcount. Optimizing performance of parallel invocations, we implement ourprotocols along with a complete set of basic operations in a library calledSEPIA. We evaluate the running time and bandwidth requirements of our protocolsin realistic settings on a local cluster as well as on PlanetLab and show thatthey work in near real-time for up to 140 input providers and 9 computationnodes. Compared to implementations using existing general-purpose MPCframeworks, our protocols are significantly faster, requiring, for example, 3minutes for a task that takes 2 days with general-purpose frameworks. Thisimprovement paves the way for new applications of MPC in the area ofnetworking. Finally, we run SEPIA-s protocols on real traffic traces of 17networks and show how they provide new possibilities for distributedtroubleshooting and early anomaly detection.

Autor: Martin Burkhart, Mario Strasser, Dilip Many, Xenofontas Dimitropoulos


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