The leading objective of COMETE is to develop a principled approach to privacy protection to guide the design of sanitization mechanisms in realistic scenarios. We aim to provide solid mathematical foundations were we can formally analyze the properties of the proposed mechanisms, considered as leading evaluation criteria to be complemented with experimental validation. In particular, we focus on privacy models that:
- allow the sanitization to be
applied and controlled directly by the user, thus avoiding the need of a trusted party as well as the risk of security breaches on the collected data,
robust with respect to combined attacks, and
- provide an
optimal trade-off between privacy and utility.
Two major lines of research are related to machine learning and social networks. These are prominent presences in nowadays social and economical fabric, and constitute a major source of potential problems. In this context, we explore topics related to the propagation of information, like
group polarization, and other issues arising from the deep learning area, like fairness and robustness with respect to adversarial inputs, that have also a critical relation with privacy.