A Survey on Discrimination Avoidance in Data Mining

ABSTRACT: For extracting useful knowledge which is hidden in large set of data, Data mining is a very important technology. There are some negative perceptions about data mining. This perception may contain unfairly treating people who belongs to some specific group. Classification rule mining technique has covered the way for making automatic decisions like loan granting/denial and insurance premium computation etc. These are automated data collection and data mining techniques. According to discrimination attributes if training data sets are biases then discriminatory decisions may ensue. Thus in data mining antidiscrimination techniques with discrimination discovery and prevention are included. It can be direct or indirect. When decisions are made based on sensitive attributes that time the discrimination is indirect. When decisions are made based on nonsensitive attributes which are strongly correlated with biased sensitive ones that time the discrimination is indirect. The proposed system tries to tackle discrimination prevention in data mining. It proposes new improved techniques applicable for direct or indirect discrimination prevention individually or both at the same time. Discussions about how to clean training data sets and outsourced data sets in such a way that direct and/or indirect discriminatory decision rules are converted to legitimate classification rules are done. New metrics to evaluate the utility of the proposed approaches are proposes and comparison of these approaches is also done

An Efficient Rule-Hiding Method for Privacy Preserving in Transactional Databases

One of the obstacles in using data mining techniques such as association rules is the risk of leakage of sensitive data after the data is released to the public. Therefore, a trade-off between the data privacy and data mining is of a great importance and must be managed carefully. In this study an efficient algorithm is introduced for preserving the privacy of association rules according to distortion-based method, in which the sensitive association rules are hidden through deletion and reinsertion of items in the database. In this algorithm, in order to reduce the side effects on non-sensitive rules, the item correlation between sensitive and non-sensitive rules is calculated and the item with the minimum influence in non-sensitive rules is selected as the victim item. To reduce the distortion degree on data and preservation of data quality, transactions with highest number of sensitive items are selected for modification. The results show that the proposed algorithm has a better performance in the non-dense real database having less side effects and less data loss compared to its performance in dense real database. Further the results are far better in synthetic databases in compared to real databases

Why Enjoying Your Fun Matters: The Role of Participation in Fun Activities, Positive Affect, and Citizenship Pressure on Knowledge Management Behaviors

Drawing from social exchange theory, this research develops a mediated-moderation model that examines the direct and indirect effects of participation in fun activities on three knowledge management behaviors (i.e., knowledge sharing, knowledge hiding, knowledge manipulating) and investigates the mediating role of positive affect and the moderating role of citizenship pressure on these relationships. A three-wave, two-source sample (n = 163) of employees belonging to a high-tech start-up in Canada is used to test this model. Results highlight the importance of positive affect by showing the effects of participation in fun activities on knowledge management behaviors is dependent on whether or not participation in fun activities leads to positive affect. Data also shows citizenship pressure moderates the direct relationship between participation in fun activities and knowledge manipulating, as well as the indirect relationship between participation in fun activities and both knowledge sharing and hiding. These results highlight the theoretical and practical importance of both positive affect and citizenship pressure in understanding the dynamic relationship between workplace fun and knowledge management