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Permanent link (DOI): https://doi.org/10.7939/R3RF5KJ5P

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A Framework for Enforcing Privacy in Mining Frequent Patterns Open Access

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Author or creator
Oliveira, Stanley
Zaiane, Osmar
Additional contributors
Subject/Keyword
Sanitizing Algorithms
Database Systems
Artifactual Patterns
Hiding Failure
Misses Cost
Data Mining and Privacy
Type of item
Computing Science Technical Report
Computing science technical report ID
TR02-13
Language
English
Place
Time
Description
Technical report TR02-13. Discovering hidden patterns from large amounts of data plays an important role in marketing, business, medical analysis, and other applications where these patterns are paramount for strategic decision making. However, recent research has shown that some discovered patterns can pose a threat to security and privacy. One alternative to address the privacy requirements in mining hidden patterns is to look for a balance between hiding restrictive patterns and disclosing non-restrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, three techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on inverted files and boolean queries; and a set of algorithms to ``sanitize'' a database. In addition, we introduce a mining association performance measure that quantifies the fraction of mining patterns which are preserved after sanitizing a database. We also report the results of performance evaluation of our research prototype and an analysis of such results.
Date created
2002
DOI
doi:10.7939/R3RF5KJ5P
License information
Creative Commons Attribution 3.0 Unported
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