PRESIDIUM

Privacy Preserving Distributed Optimization, Principal Investigator, Swedish Research Council, 2016-2019. With the recent advances in IoT and machine learning, several different approaches to data privacy have been proposed for data querying and distributed optimization, each with their own advantages and use cases. Despite these recent advances, there is no general framework for evaluating data privacy, let alone provide rigorous arguments for why data privacy is preserved. The goal of PRESIDIUM is to develop a general theory for discussing data privacy, and for proving rigorously to which degree data privacy can be ensured by common algorithms for distributed optimization.

Carlo Fischione
Carlo Fischione
Professor