A New Scheme to Privacy-Preserving Collaborative Data Mining

被引：2
|
作者：
Zhu, Jianming [1 ]
机构：
[1] Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China
来源：
FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 1, PROCEEDINGS | 2009年
关键词：
Security and privacy; data mining; privacy-preserving data mining; k-nearest neighbor classification;
D O I：
10.1109/IAS.2009.133
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：468 / 471
页数：4
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