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DAWAK
2006
Springer
15 years 2 months ago
Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures
Abstract. Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view ...
Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu...
FSKD
2008
Springer
136views Fuzzy Logic» more  FSKD 2008»
14 years 11 months ago
k-Anonymity via Clustering Domain Knowledge for Privacy Preservation
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key co...
Taiyong Li, Changjie Tang, Jiang Wu, Qian Luo, She...
TKDE
2008
119views more  TKDE 2008»
14 years 10 months ago
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
Abstract--Individual privacy will be at risk if a published data set is not properly deidentified. k-Anonymity is a major technique to deidentify a data set. Among a number of k-an...
Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu...
FGR
2011
IEEE
354views Biometrics» more  FGR 2011»
14 years 1 months ago
Hierarchical ranking of facial attributes
Abstract— We propose a novel hierarchical structured prediction approach for ranking images of faces based on attributes. We view ranking as a bipartite graph matching problem; l...
Ankur Datta, Rogerio Feris, Daniel A. Vaquero
EDBT
2004
ACM
145views Database» more  EDBT 2004»
15 years 10 months ago
CUBE File: A File Structure for Hierarchically Clustered OLAP Cubes
Abstract. Hierarchical clustering has been proved an effective means for physically organizing large fact tables since it reduces significantly the I/O cost during ad hoc OLAP quer...
Nikos Karayannidis, Timos K. Sellis, Yannis Kouvar...