A Study of Differentially Private Frequent Itemset Mining

Trupti Kenekar, A. R. Dani

Abstract: Frequent sets play an important role in many Data Mining tasks that try to search interesting patterns from databases, such as association rules, sequences, correlations, episodes, classifiers and clusters. FrequentItemsets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. In this paper differential privacy aims to get means to increase the accuracy of queries from statistical databases while minimizing the chances of identifying its records and itemset. We studied algorithm consists of a preprocessing phase as well as a mining phase. We under seek the applicability of FIM techniques on the MapReduce platform, transaction splitting. We analyzed how differentially private frequent itemset mining of existing system as well.

Keywords: Frequent itemset mining, Differential Privacy, Transaction Splitting

How to Cite?: Trupti Kenekar, A. R. Dani, "A Study of Differentially Private Frequent Itemset Mining", Volume 4 Issue 10, October 2015, International Journal of Science and Research (IJSR), Pages: 1483-1486, https://www.ijsr.net/getabstract.php?paperid=SUB159086, DOI: https://dx.doi.org/10.21275/SUB159086