Mining association rules help data owners to unveil hidden patterns from their data to analyze & predict the operation on application domain. However, mining rules in a distributed environment is not a minor task due to privacy concerns. Data owners are interested in collaborating to mine rules on different levels; however, they are concerned that sensitive information related to somebody involved in their database might get compromised during the mining process. Here formulate the problem to solving association rules queries in a environment such that the mining process is confidential and the outcomes are differentially private. Work proposes a privacy-preserving association rules mining where strong association rules are determined privately, and the results returned satisfy differential privacy. Finally done experiments on real-life data it shows that designed approach can efficiently answer association rules queries and is scalable with increasing data records.

Association rules mining, Data Privacy, Data Mining, High confidence

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