Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes
Abstract
Preserved privacy and enhanced utility are two competing requirements in data publishing. For maintaining a trade-off between the two; a plethora of research work exist in 1:1 scenario (each individual has a single record) with a single sensitive attribute (SA). However, some practical scenarios i.e., data having 1:M records (an individual can have multiple records) with multiple sensitive attributes (MSAs), have been relatively understudied. In...
Paper Details
Title
Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes
Published Date
Jul 1, 2019
Journal
Volume
488
Pages
238 - 256
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