High Confidence Association Rule for Product Selling Strategy - Kalas, Mamata S; Unne, Amruta G (International Journal of Computer Sciences and Engineering, 2019)
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.