Special Issue Article Open Access

An Improved Privacy Preserved Rule Mining for Credit Dataset with Discrimination Prevention

Abstract

Security and privacy methods are used to protect the data values. Private data values are secured with confidentiality and integrity methods. Privacy model hides the individual identity over the public data values. Sensitive attributes are protected using anonymity methods. Discrimination is the prejudicial treatment of an individual based on their membership in a certain group or category. Antidiscrimination acts are designed to prevent discrimination on the basis of a number of attributes in various settings. Public data collections are used to train association/classification rules in view of making automated decisions. Data mining can be both a source of discrimination and a means for discovering discrimination. Automated data collection and data mining techniques such as classification rule mining are used to making automated decisions. Discriminations are divided into two types such as direct and indirect discriminations. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on non sensitive attributes which are strongly correlated with biased sensitive ones. Discrimination discovery and prevention are used for anti-discrimination requirements. Direct and indirect discriminations prevention is applied on individually or both at the same time. The data values are cleaned to obtain direct and/or indirect discriminatory decision rules. Data transformation techniques are applied to prepare the data values for the discrimination prevention. Rule protection and rule generalization algorithm and direct and indirect discrimination prevention algorithm are used to protect discriminations. The discrimination prevention model is integrated with the differential privacy scheme to high privacy. Dynamic policy selection based discrimination prevention is adopted to generalize the systems for all regions. Data transformation technique is improved to increase the utility rate. Discrimination removal process is improved with rule hiding techniques.

Boopathiraja K, Nithyakalyani S M.E., (Ph.D)

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