Privacy and utility allied
With the ever-increasing use of internet-connected devices, such as computers, smart grids, IoT appliances and GPS-enabled equipments,   personal data are collected in larger and larger amounts, and then stored and manipulated  for the most diverse purposes.  Undeniably, the big-data technology   provides enormous benefits to industry, individuals and society, ranging from improving business strategies and boosting  quality of  service to enhancing  scientific progress. On the other hand, however, the collection and manipulation  of personal data raises alarming privacy issues. Both the experts and the population at large are becoming increasingly aware of the risks, due to the repeated cases of violations and leaks that keep hitting the headlines.   The objective of this project is to develop the theoretical foundations, methods and tools to protect  the privacy of the individuals while letting their data to be collected and used for statistical purposes. We aim in particular at developing mechanisms that: (1) can be applied and controlled directly by the user, thus avoiding the need of a trusted party,  (2) are  robust with respect to combination of information from different sources,   and (3) provide an optimal trade-off between privacy and utility.   We intend to pursue these goals by developing a new framework for privacy based on the addition of controlled noise  to individual data, and associated methods to recover the useful statistical information, and to protect the quality of service.