Data Discrimination Prevention using Genetic Algorithm Method in Data Mining

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-34 Number-1
Year of Publication : 2016
Authors : Kalaivani.S, Jeniffer.D, Abirami.K, Bhargavi.S
  10.14445/22315381/IJETT-V34P210

MLA 

Kalaivani. S, Jeniffer. D, Abirami. K, Bhargavi. S"Data Discrimination Prevention using Genetic Algorithm Method in Data Mining", International Journal of Engineering Trends and Technology (IJETT), V34(1),50-54 April 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Now-a-days data mining techniques are used for extracting specific kind of knowledge for discrimination purposes. This can be more commonly seen in banking sectors, where the private information of an individual such as religion, race, nationality, gender, income, age and so on can be misused. Such sensitive information are used for decision making purposes by deciding whether to grant loans, give employment, etc. Direct discrimination is based on sensitive attributes. Indirect discrimination is based on non-sensitive attributes in close relation with sensitive ones. For this reason, anti-discrimination techniques are used for discrimination discovery and prevention. In this paper, we introduce the notion of classification rule techniques combined with association rule hiding for cleaning the training data sets and also to eliminate the possibility of discrimination. We propose new techniques for tackling discrimination prevention in data mining for direct and indirect discrimination prevention, individually or both at the same time. First, we find the frequent classification rule for the training data set. Then we perform association rule hiding’s heuristic approach. Finally, the original data is transformed in such a way that direct and/or indirect discriminatory biases are removed, while also maintaining the data quality as well as reducing information loss.

 References

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Keywords
Anti-discrimination, Data mining, Direct discrimination, Indirect discrimination, Association rules, Privacy.