Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language

Investigations in Privacy Preserving Data Mining

Data Mining, Data Sharing and Privacy-Preserving are fast emerging as a field of the high level of the research study. A close review of the research based on Privacy Preserving Data Mining revealed the twin fold problems, first is the protection of private data (Data Hiding in Database) and second is the protection of sensitive rules (Knowledge) ingrained in data (Knowledge Hiding in the database). The first problem has its impetus on how to obtain accurate results even when private data is concealed. The second issue focuses on how to protect sensitive association rule contained in the database from being discovered, while non-sensitive association rules can still be mined with traditional data mining projects. Undoubtedly, performance is a major concern with knowledge hiding techniques. This paper focuses on the description of approaches for Knowledge Hiding in the database as well as discuss issues and challenges about the development of an integrated solution for Data Hiding in Database and Knowledge Hiding in Database. This study also highlights directions for the future studies so that suggestive pragmatic measures can be incorporated in ongoing research process on hiding sensitive association rules

Survey on Noise Estimation and Removal Methods through SVM

The Support vector machine is statistical learning method but it is also recognized as another approach to solve and simplify data classification. SVM have been discovered as one of the successful classification techniques for many areas and application and it works on different datasets and gives appropriate result. There is a noise or irrelevant data present in datasets which leads to poor result so to remove those meaningless data some approaches are introduced for better result. In this paper an introduction of SVM (Support Vector Machine) and various noise estimation and noise removal methods based on support vector machine is presented