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, with-out 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…

• CU
  City University of Hong Kong