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

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
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.
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