Discrimination Prevention with Classification and Privacy Preservation in Data mining

Abstract

Mining of data is an important increasingly methodology for extracting and finding the meaningful hidden knowledge in huge archives of data. There are the various negative social perceptions related to mining of data, out of which many are potential discrimination and potential privacy invasion. The potential discrimination consists of unfairly treating and identifying people based on their existence and belonging to a particular group. Data mining and automated data collection techniques such as classification and association rule mining have provided way to taking decisions automatically, such as computation of insurance premium, loan granting or denial, credit card issue etc. If the provided data sets for training are biased in discriminatory (sensitive) attributes such as, race, gender, religion, etc., discriminatory decisions can be taken and may ensue. For avoiding this situations, antidiscrimination methodology like discrimination prevention and discovery have been considered in the data mining. There are mainly two types of discrimination, one is direct discrimination and second is indirect discrimination. Direct discrimination exists in the situations when decisions are taken on the basis of the sensitive attributes. Indirect discrimination exists in the situations when decisions are taken on the basis of the non-sensitive attributes that are strongly correlated with the biased sensitive attributes.