Reducing communication cost in a privacy preserving distributed association rule mining

Abstract

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, discovery 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 data mining research community. Since distributed association mining discovers global 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 global association rules without revealing confidential inputs such as statistical properties of..