We consider the problem of private computation of approximate Heavy Hitters. Alice and Bob each hold a vector and, in the vector sum, they want to find the B largest values along with their indices. While the exact problem requires linear communication, protocols in the literature solve this problem approximately using polynomial computation time, polylogarithmic communication, and constantly many rounds. We show how to solve the problem privately with comparable cost, in the sense that nothing is learned by Alice and Bob beyond what is implied by their input, the ideal top-B output, and goodness of approximation (equivalently, the Euclidean norm of the vector sum). We give lower bounds showing that the Euclidean norm must leak by any efficient algorithm.
Other work in private communication-efficient protocols for specific functions includes the Private Information Retrieval problem @cite_9 @cite_6 @cite_2 , building decision trees @cite_10 , set intersection and matching @cite_12 , and @math 'th-ranked element @cite_0 .
@cite_9 @cite_6 @cite_0 @cite_2 @cite_10 @cite_12
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We establish the following, quite unexpected, result: replication of data for the computational private information retrieval problem is not necessary. More specifically, based on the quadratic residuosity assumption, we present a single database, computationally private information retrieval scheme with O(n sup spl epsiv ) communication complexity for any spl epsiv >0. Given two or more parties possessing large, confidential datasets, we consider the problem of securely computing the k th -ranked element of the union of the datasets, e.g. the median of the values in the datasets. We investigate protocols with sublinear computation and communication costs. In the two-party case, we show that the k th -ranked element can be computed in log k rounds, where the computation and communication costs of each round are O(log M), where log M is the number of bits needed to describe each element of the input data. The protocol can be made secure against a malicious adversary, and can hide the sizes of the original datasets. In the multi-party setting, we show that the k th -ranked element can be computed in log M rounds, with O(s log M) overhead per round, where s is the number of parties. The multi-party protocol can be used in the two-party case and can also be made secure against a malicious adversary. We present a single-database computationally private information retrieval scheme with polylogarithmic communication complexity. Our construction is based on a new, but reasonable intractability assumption, which we call the φ-Hiding Assumption (φHA): essentially the difficulty of deciding whether a small prime divides φ(m), where m is a composite integer of unknown factorization. In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated by the need both to protect privileged information and to enable its use for research or other purposes. The above problem is a specific example of secure multi-party computation and, as such, can be solved using known generic protocols. However, data mining algorithms are typically complex and, furthermore, the input usually consists of massive data sets. The generic protocols in such a case are of no practical use and therefore more efficient protocols are required. We focus on the problem of decision tree learning with the popular ID3 algorithm. Our protocol is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth. 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 semi-honest 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.