Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Data mining for discrimination discovery
ACM Transactions on Knowledge Discovery from Data (TKDD)
Integrating induction and deduction for finding evidence of discrimination
Artificial Intelligence and Law
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such widespread data collection efforts coupled with powerful data analysis tools raised concerns about privacy. This is due to the fact that collected data may contain confidential information. One method to ensure privacy is to selectively hide confidential information from the data sets to be disclosed. In this paper, we focus on hiding confidential correlations. We introduce a heuristic to reduce the information loss and propose a blocking method that prevents discovery of confidential correlations while preserving the usefulness of the data set.