Paper Info
Title
Rating: Privacy Preservation for Multiple Attributes with Different Sensitivity Requirements
Abstract
Motivated by the insufficiency of the existing framework that could not process multiple attributes with different sensitivity requirements on modeling real world privacy requirements for data publishing, we present a novel method, rating, for publishing sensitive data. Rating releases AT (Attribute Table) and IDT (ID Table) based on different sensitivity coefficients for different attributes. This approach not only protects privacy for multiple sensitive attributes, but also keeps a large amount of correlations of the micro data. We develop algorithms for computing AT and IDT that obey the privacy requirements for multiple sensitive attributes, and maximize the utility of published data as well. We prove both theoretically and experimentally that our method has better performance than the conventional privacy preserving methods on protecting privacy and maximizing the utility of published data. To quantify the utility of published data, we propose a new measurement named classification measurement.
Year
DOI
Venue
2011
10.1109/ICDMW.2011.144
ICDM Workshops
Keywords
Field
DocType
multiple sensitive attribute,data publishing,multiple attributes,published data,real world privacy requirement,different sensitivity requirements,micro data,privacy preservation,privacy requirement,sensitive data,conventional privacy,different sensitivity coefficient,different attribute,data privacy
Data mining,Computer science,Data publishing,Information privacy,Privacy software,Database
Conference
Citations 
PageRank 
References 
9
0.59
13
Authors
3
Name
Order
Citations
PageRank
Jinfei Liu19111.12
Jun Luo222226.61
Joshua Zhexue Huang3136582.64