Two methods for privacy preserving data mining with malicious participants

被引:18
|
作者
Shah, Divyesh [1 ]
Zhong, Sheng [1 ]
机构
[1] SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY 14260 USA
关键词
data mining; privacy; malicious model; attacks; protocols;
D O I
10.1016/j.ins.2007.07.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Privacy preserving data mining addresses the need of multiple parties with private inputs to run a data mining algorithm and learn the results over the combined data without revealing any unnecessary information. Most of the existing cryptographic solutions to privacy-preserving data mining assume semi-honest participants. In theory, these solutions can be extended to the malicious model using standard techniques like commitment schemes and zero-knowledge proofs. However, these techniques are often expensive, especially when the data sizes are large. In this paper, we investigate alternative ways to convert solutions in the semi-honest model to the malicious model. We take two classical solutions as examples, one of which can be extended to the malicious model with only slight modifications while another requires a careful redesign of the protocol. In both cases, our solutions for the malicious model are much more efficient than the zero-knowledge proofs based solutions. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:5468 / 5483
页数:16
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