A New Scheme to Privacy-Preserving Collaborative Data Mining

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.