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A New Scheme to Privacy-Preserving Collaborative Data Mining

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1 Author(s)
Jianming Zhu ; Sch. of Inf., Central Univ. of Finance & Econ., Beijing, China

Protection of privacy has become an important problem in data mining. In this paper, we present a new scheme to privacy-preserving collaborative data mining based on the homomorphic encryption and ElGamal encryption system in distributed environment. This scheme can be used to compute the k-nearest neighbor search. Our scheme is provable secure and efficient and can prevent colluded attacker. Comparing with the previous work on this issue, our method can be used in multi-parties who want to cooperatively compute the answers without revealing to each other their identity and their private data.

Published in:

Information Assurance and Security, 2009. IAS '09. Fifth International Conference on  (Volume:1 )

Date of Conference:

18-20 Aug. 2009