Title :
A New Scheme to Privacy-Preserving Collaborative Data Mining
Author_Institution :
Sch. of Inf., Central Univ. of Finance & Econ., Beijing, China
Abstract :
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.
Keywords :
cryptography; data mining; data privacy; ElGamal encryption system; homomorphic encryption; k-nearest neighbor search; privacy protection; privacy-preserving collaborative data mining; Clustering algorithms; Collaboration; Collaborative work; Cryptography; Data mining; Data privacy; Data security; Information security; Perturbation methods; Protection; Security and privacy; data mining; k-nearest neighbor classification; privacy-preserving data mining;
Conference_Titel :
Information Assurance and Security, 2009. IAS '09. Fifth International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-0-7695-3744-3
DOI :
10.1109/IAS.2009.133
