Privacy beyond single sensitive attribute

• Main Authors: FANG, Yuan, ASHRAFI, Mafruz Zaman, NG, See Kiong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Data quality; General solutions; K-Anonymity; Loss ratio; Microdata; Sensitive attribute; T-closeness; Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/4062; https://ink.library.smu.edu.sg/context/sis_research/article/5065/viewcontent/Privacy_beyond_single_sensitive_attribute_2011.pdf
• Institution: Singapore Management University

Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on ℓ-diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microdata with multiple SAs that may be multi-valued. This paper explores the issue of attribute disclosure in such scenarios. We propose a method called CODIP (Complete Disjoint Projections) that outlines a general solution to deal with the shortcomings in a naïve approach. We also introduce two measures, Association Loss Ratio and Information Exposure Ratio, to quantify data quality and privacy, respectively. We further propose a heuristic CODIP*for CODIP, which obtains a good trade-off in data quality and privacy. Finally, initial experiments show that CODIP*is practically useful on varying numbers of SAs.