T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing

DOI10.1287/IJOC.2017.0791zbMath1461.91176OpenAlexW2891943569MaRDI QIDQ5136073

Zhengrui Jiang, Haifang Yang, Yu Zhang, Ming-Zheng Wang

Publication date: 25 November 2020

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.2017.0791

zbMATH Keywords

privacy preservationtransactional datadata publishingslicing technique\(t\)-closeness model

Mathematics Subject Classification ID

Economics of information (91B44)

Related Items (1)

A survey of privacy-preserving mechanisms for heterogeneous data types

Cites Work

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• Adaptive data reduction for large-scale transaction data
• Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining
• Finding Groups in Data
• k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
• ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION

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