Personalized privacy preservation

TLDR

Existing methods apply a universal privacy level that fails to meet individual needs, offering insufficient protection to some and excessive control to others. This study introduces a personalized anonymity framework to generalize sensitive data while preserving privacy. The authors develop the framework, conduct a theoretical analysis, and validate it with extensive experiments. The personalized approach achieves minimal generalization, retains more information, and outperforms prior universal methods by satisfying all privacy requirements.

Abstract

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 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 extensive experiments.