Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
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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Discovering frequent patterns from huge amounts of data is one of the most studied problems in data mining. However, some sensitive patterns with security policies may cause a threat to privacy. We investigate to find an appropriate balance between a need for privacy and information discovery on frequent patterns. By multiplying the original database and a sanitization matrix together, a sanitized database with privacy concerns is obtained. Additionally, a probability policy is proposed to against the recovery of sensitive patterns and reduces the modifications of the sanitized database. A set of experiments is also performed to show the benefit of our work.