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Permanent link (DOI): https://doi.org/10.7939/R3V40K16C

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

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Author or creator
Oliveira, Stanley
Zaiane, Osmar
Additional contributors
Subject/Keyword
Sensitive Knowledge
Sliding Window Algorithm
Database Systems
Data Sanitization
Privacy Preserving Data Mining
Type of item
Computing Science Technical Report
Computing science technical report ID
TR03-15
Language
English
Place
Time
Description
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.
Date created
2003
DOI
doi:10.7939/R3V40K16C
License information
Creative Commons Attribution 3.0 Unported
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