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DARM: a privacy-preserving approach for distributed association rules mining on horizontally-partitioned data

Extracting association rules helps data owners to unveil hidden patterns from their data for the purpose of analyzing and predicting the behavior of their clients. However, mining association rules in a distributed environment is not a trivial task due to privacy concerns. Data owners are interested in collaborating with each other to mine association rules on a global level; however, they are concerned that sensitive information related to the individuals involved in their database might get compromised during the mining process. In this paper, we formulate and address the problem of answering association rules queries in a distributed environment such that the mining process is confidential and the results are differentially private. We propose a privacy-preserving distributed association rules mining approach, named DARM, where global strong association rules are determined in a confidential way, and the results returned satisfy ε-differential privacy. We conduct our experiments on real-life data, and show that our approach can efficiently answer association rules queries and is scalable with increasing data records.