A more well-founded security proof of the privacy-preserving distributed mining of association rules protocols
Feng Zhang, Gansen Zhao · 2009
Privacy-preserving data mining aims at discovering beneficial information from a large amount of data without violating the privacy policy. Privacy-preserving association rules mining research has already generated many interesting results. Based on commutative encryptions and the Secure Multi-party Computation (SMC) theory, Kantarcioglu and Clifton [1] propose two protocols to implement privacy-preserving mining of association rules over horizontally partitioned data. The paper addresses its incorrect security proof and introduces a more well-founded proof. This paper also identifies several other errors in [1]. This kind of protocols and their proof are a concrete application of Secure Multi-party Computation, which is be of great significances to the privacy-preserving data mining studies based on SMC. Thus establishment of the correct proof methodology is important. This paper demonstrates the correct proof methodology by correcting the fault proof in [1]