Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Computational disclosure control: a primer on data privacy protection
Computational disclosure control: a primer on data privacy protection
IEEE Transactions on Knowledge and Data Engineering
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Applied Intelligence
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Suppressing Data Sets to Prevent Discovery of Association Rules
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
Discrimination-aware data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
k-NN as an implementation of situation testing for discrimination discovery and prevention
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rule protection for indirect discrimination prevention in data mining
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
A study of top-k measures for discrimination discovery
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Discrimination discovery in scientific project evaluation: A case study
Expert Systems with Applications: An International Journal
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In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this article, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, for example, census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this article, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset.