Hiding Sensitive Association Rules over Privacy Preserving Distributed Data Mining

Abstract

The problem of Privacy-Preserving Data Mining (PPDM) has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms. A number of techniques have been suggested in recent years in order to perform PPDM. These techniques are used to study different transformation methods associated with privacy. In this paper, a system for PPDDM of association rules is proposed. This system works under the common and realistic assumptions that parties are semi-honest, Semi-Trusted Third Party (STTP) and the databases are horizontally distributed over these parties. A new algorithm for hiding sensitive rules is presented in this system. The experimental results for this algorithm have shown that it has good hiding accuracy with an acceptable level of side effects when it compared with the same algorithm in centralized system and other existing algorithms in distributed database system. Furthermore, the proposed system uses the Secure Socket Layer (SSL) with commutative encryption to support the certifications and security over system various components.