CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING

Abstract

Data mining process are followed an extensive undertaking of research and product improvement. This development started when enterprise material was first loaded on computers, continued with advancement in data access, and more recently, developed technologies that permit users to transport through their data in real time. APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. In this report, I study the data structure, implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. However, a fundamental issue, and still not adequately examined, is demand to balance the privacy of the revealed data with the legitimate needs of the data users. The rule is characterizing as sensitive if its disclosure threat is above a certain privacy threshold. Sometimes, sensitive rules should not disclose to the public, since among other things, they may use for inferring sensitive data, or they may offer enterprise competitors with an advantage. Therefore, next I worked with some association rule hiding algorithms and examined their performances to analyse their time complexity orderly and the affect that they have in the original database.