It is not uncommon in the data anonymization literature to oppose the “old” k k-anonymity
model to the “new” differential privacy model, which offers more robust privacy guarantees …

Being able to release and exploit open data gathered in information systems is crucial for
researchers, enterprises and the overall society. Yet, these data must be anonymized before …

This paper aims at answering the following two questions in privacy-preserving data
analysis and publishing. The first is: What formal privacy guarantee (if any) does k …

Privacy-preserving microdata publishing currently lacks a solid theoretical foundation. Most
existing techniques are developed to satisfy syntactic privacy notions such as k-anonymity …

Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of
data subjects in microdata releases. It has been used as an alternative to generalization and …

OpenData movement around the globe is demanding more access to information which lies
locked in public or private servers. As recently reported by a McKinsey publication, this data …

Set-valued data provides enormous opportunities for various data mining tasks. In this
paper, we study the problem of publishing set-valued data for data mining tasks under the …

k-Anonymity and e-differential privacy are two main privacy models proposed within the
computer science community. Whereas the former was proposed for privacy-preserving data …

Abstract k-anonymity and ε-differential privacy are two mainstream privacy models, the
former introduced to anonymize data sets and the latter to limit the knowledge gain that …

Data privacy has been an important research topic in the security, theory and database
communities in the last few decades. However, many existing studies have restrictive …