Abstract.We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. This problem has many applications for online collaboration. We present protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. For lists of length k, we obtain O(k) communication overhead and O(k ln ln k) computation. The protocol for the semihonest environment is secure in the standard model, while the protocol for the malicious environment is secure in the random oracle model. We also consider the problem of approximating the size of the intersection, show a linear lower-bound for the communication overhead of solving this problem, and provide a suitable secure protocol. Lastly, we investigate other variants of the matching problem, including extending the protocol to the multi-party setting as well as considering the problem of approximate matching.

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Ideally, enterprise administrators could specify fine-grain policies that drive how the underlying switches forward, drop, and measure traffic. However, existing techniques for flowbased networking rely too heavily on centralized controller software that installs rules reactively, based on the first packet of each flow. In this paper, we propose DIFANE, a scalable and efficient solution that keeps all traffic in the data plane by selectively directing packets through intermediate switches that store the necessary rules. DIFANE relegates the controller to the simpler task of partitioning these rules over the switches. DIFANE can be readily implemented with commodity switch hardware, since all data-plane functions can be expressed in terms of wildcard rules that perform simple actions on matching packets. Experiments with our prototype on Click-based OpenFlow switches show that DI-FANE scales to larger networks with richer policies.

Abstract. We study the problem of privacy-preserving access to a database. Particularly, we consider the problem of privacy-preserving keyword search (KS), where records in the database are accessed according to their associated keywords and where we care for the privacy of both the client and the server. We provide efficient solutions for various settings of KS, based either on specific assumptions or on general primitives (mainly oblivious transfer). Our general solutions rely on a new connection between KS and the oblivious evaluation of pseudorandom functions (OPRFs). We therefore study both the definition and construction of OPRFs and, as a corollary, give improved constructions of OPRFs that may be of independent interest.