Privacy-Enhancing Distributed Higher-Order ARM

Traditional association rule mining algorithms assume that data instances are independent and identically distributed (IID) [29]. In statistical relational learning (SRL), however, relationships between instances can be leveraged to improve performance of learning algorithms [2]. Higher-order association rule mining is an example of a SRL approach that does not make the IID assumption, but instead discovers itemsets that cross record boundaries [21]. Empirical analysis shows that higher-order methods perform especially well on small datasets as they are able to capture the variability of the underlying data distribution more readily than traditional methods [11]. In a distributed environment, however the discovery of higher-order itemsets reveals significant information about the nature of disparate data sources [21]. Preserving privacy in a setting in which data instances are treated as nodes in a graph rather than independent entities is an open problem in privacy research that has only recently received attention in the data mining community [24]. In this paper we propose a novel privacy-enhancing distributed higher-order ARM algorithm, PE-DiHO ARM. PE-DiHO ARM discovers itemsets from distributed data with a hybrid (non-horizontal, non-vertical) distribution while significantly limiting the amount of private data that is revealed. To demonstrate the validity of the approach we compare it to a non-privacy enhancing higher-order ARM algorithm [21] in an evaluation framework based on supervised learning [23]. Experimental results confirm that privacy can be significantly enhanced during the computation of higherorder itemsets in a distributed environment without significantly impacting performance. In future work we plan to apply these techniques to data provided by our law enforcement partners.