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BIOSIG
2009
75views Biometrics» more  BIOSIG 2009»
13 years 10 months ago
Multi-Sample Fusion with Template Protection
: The widespread use of biometrics and its increased popularity introduces privacy risks. In order to mitigate these risks, solutions such as the helper-data system, fuzzy vault, f...
Emile Kelkboom, Jeroen Breebaart, Raymond N. J. Ve...
ICDE
2007
IEEE
115views Database» more  ICDE 2007»
14 years 10 months ago
MultiRelational k-Anonymity
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much...
Mehmet Ercan Nergiz, Chris Clifton, A. Erhan Nergi...
TDP
2008
160views more  TDP 2008»
13 years 9 months ago
Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing
Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-ano...
Xiaoxun Sun, Hua Wang, Jiuyong Li, Traian Marius T...
WPES
2004
ACM
14 years 2 months ago
Privacy management for portable recording devices
The growing popularity of inexpensive, portable recording devices, such as cellular phone cameras and compact digital audio recorders, presents a significant new threat to privac...
J. Alex Halderman, Brent R. Waters, Edward W. Felt...
KDD
2004
ACM
159views Data Mining» more  KDD 2004»
14 years 2 months ago
Optimal randomization for privacy preserving data mining
Randomization is an economical and efficient approach for privacy preserving data mining (PPDM). In order to guarantee the performance of data mining and the protection of individ...
Michael Yu Zhu, Lei Liu