Improving the Performance of Association Rules Hiding using Hybrid Optimization Algorithm. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Murthy, T. Satyanarayana; Roy, Mettu Sumender; Varma, Mohan Krishna
• Is Part Of: Journal of applied security research. Volume 15:Issue 3(2020)
• Page Start: 423
• Page End: 437
• Publication Date: 2020-07-02
• Subjects: Association rule hiding -- hiding failure -- lost rule -- ghost rule; Police, Private -- Training of -- Periodicals; Police, Private -- Training of -- United States -- Periodicals; Private security services -- Periodicals; 363.289
• DOI: 10.1080/19361610.2020.1756155
• Languages: English
• ISSNs: 1936-1610

Abstract

Abstract: In this digital world, information evolves from several sources like social media, e-commerce sites, and mobile networks, etc., in large volumes for processing. The sensitiveness in the form of rules extracted from different resources entails that privacy-preserving is a significant research issue to be cared for. In this context, it is imperative to impose confidentiality on sensitive rule data during its processing. Optimization algorithms play a vital role in the reduction of ghost rules and lost rules in association rule hiding. This paper proposes a novel Hybrid optimization algorithm that acquires the characteristics of the above-said algorithms for association rule hiding and it has been shown that it produces better results in less time. Further, the newly introduced concepts on the lost rule generation and recovery are seen to produce almost 99% of lost rules with a reduction in side effect factors from 24–5%.