Personalized privacy preservation

10 years 1 months ago
Personalized privacy preservation
We study generalization for preserving privacy in publication of sensitive data. The existing methods focus on a universal approach that exerts the same amount of preservation for all persons, without catering for their concrete needs. The consequence is that we may be offering insufficient protection to a subset of people, while applying excessive privacy control to another subset. Motivated by this, we present a new generalization framework based on the concept of personalized anonymity. Our technique performs the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the microdata. We carry out a careful theoretical study that leads to valuable insight into the behavior of alternative solutions. In particular, our analysis mathematically reveals the circumstances where the previous work fails to protect privacy, and establishes the superiority of the proposed solutions. The theoretical findings are verified with...
Xiaokui Xiao, Yufei Tao
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2006
Authors Xiaokui Xiao, Yufei Tao
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