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Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built(More)
Secure Multi-party Computation (SMC) problems deal with the following situation: Two (or many) parties want to jointly perform a computation. Each party needs to contribute its private input to this computation, but no party should disclose its private inputs to the other parties, or to any third party. With the proliferation of the Internet, SMC problems(More)
— One of the most substantial ways to protect users' sensitive information is encryption. This paper is about the keyword index search system on encrypted documents. It has been thought that the search with errors over encrypted data is impossible because 1 bit difference over plaintexts may reduce to enormous bits difference over cyphertexts. We propose a(More)
In this paper we introduce a framework for privacy-preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the(More)
— A secure multiparty number-product protocol is an important building block in the area of secure multiparty computation. With proper composition of the building block, most applications, such as circuit evaluation, data mining, and private information retrieval, can be executed securely and collaboratively by potentially dishonest parties. In this work,(More)