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A research-oriented monograph that investigates methods to hide sensitive association rules in data mining outputs to preserve privacy while still enabling useful data analysis. The book presents formal definitions of privacy, algorithms for sanitizing databases, evaluation metrics for data-utility tradeoffs, and theoretical and experimental results — useful for researchers and data scientists working on privacy-preserving data mining and secure data sharing.

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245: $a: Association Rule Hiding for Data Mining
245: $b:
082: $a:
100: $a: Aris Gkoulalas-Divanis & Vassilios S. Verykios
650: $a: Data mining
250: $a:
440: $v:
250: $b: Springer Science+Business Media, LLC
250: $c: 2010
020: $a: 978-1-4419-6568-4
300: $a: 224