Efficient Multidimensional Suppression for K-Anonymity

• Published in: IEEE transactions on knowledge and data engineering Vol. 22; no. 3; pp. 334 - 347
• Main Authors: Kisilevich, S., Rokach, L., Elovici, Y., Shapira, B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2010; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Classification; Classification tree analysis; Computer science; control theory; systems; Data mining; Data privacy; Data processing. List processing. Character string processing; Decision trees; deindentified data; Delta modulation; Diseases; Exact sciences and technology; Hierarchies; Information retrieval. Graph; Information systems. Data bases; k-anonymity; Memory and file management (including protection and security); Memory organisation. Data processing; Multidimensional systems; National security; Preserving; Privacy-preserving data mining; Releasing; Software; Studies; Taxonomy; Theoretical computing; Trees; Private life; Data privacy; Data analysis; Taxonomy; Anonymity; Information extraction; Graph theory; Data mining; Decision tree; Identifier; Data integrity; Semantics; Classification; decision trees; Database; Privacy-preserving data mining; deindentified data; k-anonymity

Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being rele...