Towards privacy preserving distributed association rule mining

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns. However, discovery of such hidden patterns may disclose some sensitive information. As a result privacy becomes one of the prime concerns in data mining research. Since distributed association mining discovers global association rules by combining models from various distributed sites hence it breaches data privacy more often than it does in the centralized environments. In this work we present a methodology that generates global association rules without revealing confidential inputs of individual sites. One of the important outcomes of the proposed technique is that, it has an ability to minimize the collusion problem. Furthermore, the global model generated by this method is based on the exact global support of each itemsets, which is indeed a desirable property of distributed association rule mining.