An Efficient One-Scan Sanitization For Improving The Balance Between Privacy And Knowledge Discovery

• Author(s) / Creator(s)

  • Oliveira, Stanley
  • Zaiane, Osmar

• Technical report TR03-15. In this paper, we address the problem of protecting some sensitive knowledge in transactional databases. The challenge is on protecting actionable knowledge for strategic decisions, but at the same time not losing the great benefit of association rule mining. To accomplish that, we introduce a new, efficient one-scan algorithm that meets privacy protection and accuracy in association rule mining, without putting at risk the effectiveness of the data mining per se. Our experiments demonstrate that our algorithm is effective and achieves significant improvement over the other approaches presented in the literature. We report the main results of our performance evaluation and discuss some open research issues. | TRID-ID TR03-15

• Date created

  2003

• Subjects / Keywords

  • Sliding Window Algorithm
  • Sensitive Knowledge
  • Data Sanitization
  • Privacy Preserving Data Mining
  • Database Systems

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3V40K16C

• License

  Attribution 3.0 International