Securing Informative Fuzzy Association Rules using Bayesian Network

Muhammad Fahad,Khalid Mahmood,Khalid Iqbal,Somaiya Khatoon

doi:10.14569/ijacsa.2019.0101068

Abstract

In business association rules being considered as important assets, play a vital role in its productivity and growth. Different business partnership share association rules in order to explore the capabilities to make effective decision for enhance-ment of business and core capabilities. The fuzzy association rule mining approach emerged out of the necessity to mine quantitative data regularly present in database. An association rule is sensitive when it violates few rules and regulation for sharing particular nature of information to third world. Like classical association rules, there is a need for some privacy measures to be taken for retaining the standards and importance of fuzzy association rules. Privacy preservation is used for valu-able information extraction and minimizing the risk of sensitive information disclosure. Our proposed model mainly focuses to secure the sensitive information revealing association rules. In our model, sensitive fuzzy association rules are secured by identifying sensitive fuzzy item to perturb fuzzified dataset. The resulting transformed FARs are analyzed to conclude/calculate the accuracy level of our model in context of newly generated fuzzy association rules, hidden rules and lost rules. Extensive experiments are carried out in order to demonstrate the results of our proposed model. Privacy preservation of maximum number of sensitive FARs by keeping minimum perturbation highlights the significance of our model.

Highlights

Data mining is a systematic way for extracting useful information from large repositories of data using many effective tools and techniques [1]
Association rules which satisfy the required support confidence threshold, such rules are considered as interesting rules. These measures have been focused by researchers to improve privacy of association rules by manipulating support, confidence such as process of increasing support of antecedent of rules (ISL) and decrease in support of consequent of rule (DSR) [10], Decrease of Support Rule (DSR) and manipulation with support of LHS and RHS of rule [11], Decrease Support Confidence (DSC) algorithm introducing Pi-tree in [12], algorithm dealing with support and confidence framework naming Decrease Support of Sensitive Items (DSSI) [13] for improving issues of [12], [10]
Privacy preservation turns out to be an important aspect in data mining to restrict the disclosure of sensitive information after mining process

Summary

Introduction

Data mining is a systematic way for extracting useful information from large repositories of data using many effective tools and techniques [1]. Limitations in the support and confidence-based methodologies gave researchers the path to work on perturbation and other different approaches [8], [23] for minimizing the limitation of aforementioned framework-based techniques and enhancing the work These various techniques result into some flaws and limitation of generating lost and ghost rules, multiple database scans, incomplete transformation of database and side effects of adding noise in original database. Proposed agenda comprise of few steps; using fuzzy logic and membership function for preprocessing of original data set and converting it into fuzzy dataset, preparing input for K2 algorithm and apriori algorithm, determining the sensitive node/item using Bayesian network developed by K2 algorithm, hiding the considered sensitive rules, performing minimum perturbation for max. K2 algorithm discovers relationship among items/nodes in an increasing order

Methods

Discussion

Conclusion