To design a privacy-preserving data publishing system, we must first quantify the very notion of privacy, or information loss. In the past few years, there has been a proliferation of measures of privacy, some based on statistical considerations, others based on Bayesian or information-theoretic notions of information, and even others designed around the limitations of bounded adversaries. In t...

This work introduces a new concept that addresses the problem of preserving privacy when anonymising and publishing personal data collections. In particular, a maximum entropy oriented algorithm to protect sensitive data is proposed. As opposed to k-anonymity, l-diversity and t-closeness, the proposed algorithm builds equivalence classes with possibly uniformly distributed sensitive attribute v...

Privacy Preserving Data Publishing (PPDP) is a research field that deals with the development of methods to enable publishing of data while minimizing distortion, for maintaining usability on one hand, and respecting privacy on the other hand. Sequential release is a scenario of data publishing where multiple releases of the same underlying table are published over a period of time. A violation...

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual’s mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at...

Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet anoth...