Privacy preserving frequent pattern mining technique on encrypted cloud data
Rupali Bichitkar, VV Jagtap
Privacy preservation in data mining has gained significant recognition because of the increased concerns to ensure privacy of sensitive information. It enables multiple parties to conduct collaborative data mining while preserving the privacy of their data. Through the use of data mining techniques on this large data set, accuracy increases in terms of data result and efficiency. But it also involves the possibility of data leakage of confidential private data sets. Techniques for data mining, in particular sequential pattern mining, can be used to extract frequent patterns. Traditional cryptographic methods use encryption techniques or secure multiparty computation (SMC) to ensure privacy of data. But privacy in these techniques is at the expense of additional communication cost, which limits their use in practical applications. Therefore, in this proposed work we focus on the privacy and efficiency of frequent Item set. The proposed system uses the FP growth algorithm, which is the best performing algorithm for frequent pattern mining. To maintain the privacy of common item set patterns, the encryption algorithm has also been implemented.