Closeness: A New Privacy Measure for Data Publishing

The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that...

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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 22; číslo 7; s. 943 - 956
Hlavní autoři:	Ninghui Li, Tiancheng Li, Venkatasubramanian, Suresh
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.07.2010 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Applied sciences Computer science; control theory; systems data anonymization Data privacy Data processing. List processing. Character string processing data publishing Data security Diseases Exact sciences and technology Information security Mathematical models Memory and file management (including protection and security) Memory organisation. Data processing Privacy Privacy preservation Probability distribution Publishing Remuneration Software Tables (data) Thresholds Utilities Private life Data privacy Privacy preservation Microdata data security Anonymity Probability distribution data anonymization Modeling Equivalence classes Statistical databases data publishing Computer security Probability measure
ISSN:	1041-4347, 1558-2191
On-line přístup:	Získat plný text
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