Preemptive Measures against Malicious Party in Privacy-Preserving Data Mining

Currently, many privacy-preserving data mining (PPDM) algorithms assume the semi-honest model and/or malicious model of multi-party interaction. However, both models are far from able to fully capture the complexity of events and data interactions among parties in the process of secure data mining. In the paper, we study the problem of security violations when a malicious party provides false data. We identify four privacy vulnerabilities of secure scalar product protocols that underlie many current PPDM algorithms. We propose a general more model of two-party interaction and demonstrate its applicability to securely compute (x1 + y1)(x2 + y2) and (x + y) log2(x + y) where xi and yi are private values held by each party respectively. We show how the proposed model can be used to securely compute four commonly used kernel functions and other common functions. We also propose two necessary conditions and two basic measures that should be adopted in the current malicious model.

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Shuguo Han, Wee Keong Ng
• Keywords: Data Mining, Malicious Model, Privacy-preserving Data Mining, SDM 2008