PPDAM: Privacy-Preserving Distributed Association-Rule-Mining Algorithm

Abstract

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, the discovering of such hidden patterns has statistical meaning and may often disclose some sensitive information. As a result, privacy becomes one of the prime concerns in the data-mining research community. Since distributed association mining discovers association rules by combining local models from various distributed sites, breaching data privacy happens more often than it does in centralized environments. In this work, we present a methodology that generates association rules without revealing confidential inputs such as statistical properties of i..