Results 41 to 50 of about 7,115,390 (197)
k-Anonymization by Freeform Generalization [PDF]
Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, 2015 Syntactic data anonymization strives to (i) ensure that an adversary cannot identify an individual's record from published attributes with high probability, and (ii) provide high data utility. These mutually conflicting goals can be expressed as an optimization problem with privacy as the constraint and utility as the objective function.
Katerina Doka +3 more
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Achieving k-anonymity by clustering in attribute hierarchical structures
, 2006 . Individual privacy will be at risk if a published data set is not properly deidentified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in ...
Raymond Chi-wing Wong +7 more
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On the Complexity of Optimal k-Anonymity: A New Proof Based on Graph Coloring
IEEE AccessPrivacy is a complex balancing problem between risks and utility of data. K-anonymity, a fundamental model for preserving privacy, guarantees that an item cannot be differentiated from at least k-1 other items.
Yavuz Canbay
doaj +1 more source
Extended K-Anonymity Models Against Attribute Disclosure
, 2009 P-sensitive K-anonymity model has been recently defined as a sophistication of K-anonymity. This new property requires that there be at least P distinct values for each sensitive attribute within the records sharing a combination of key attributes ...
Xiaoxun Sun +7 more
core +1 more source
Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
Algorithms, 2018 In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data.
Elias Dritsas +3 more
doaj +1 more source
Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark
Algorithms, 2020 Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection.
Elias Dritsas +5 more
doaj +1 more source
[Name Withheld]: Anonymity and Its Implications [PDF]
, 2006 Anonymity allows the individual to have a voice without having a name. Since the word “anonymous” entered the English language with the advent of the printing press, the implications of being anonymous - and its lexical offspring “anonymity” - have ...
Weicher, Maureen
core
On the Effectiveness of k-Anonymity Against Traffic Analysis and Surveillance [PDF]
, 2006 The goal of most research on anonymity, including all currently used systems for anonymity, is to achieve anonymity through unlinkability: an adversary should not be able to determine the correspondence between the input and output messages of the system.
Hopper, Nicholas +3 more
core +1 more source
A Differentially Private (Random) Decision Tree without Noise from k-Anonymity
Applied SciencesThis paper focuses on the relationship between decision trees, a typical machine learning method, and data anonymization. It is known that information leaked from trained decision trees can be evaluated using well-studied data anonymization techniques ...
Atsushi Waseda, Ryo Nojima, Lihua Wang
doaj +1 more source