The recent advances of technology, that make the storage and processing of large amount of data affordable, have led to increasing collect of sensitive data. A consequence of the Big Data phenomenon is that the traditional definition of what is personal information, used to define boundary of privacy, seems inaccurate. Indeed, “anonymized” dataset can be massively re-identify, and increases further desanonymization in a vicious circle. Differential Privacy has been proposed as a more relevant way ta measure to what extent a contribution to a database threatens the individual, and it has become the gold standard in Machine Learning research. I will briefly present the key points of the definition, highlight its strengths and the issues that are imposed by this framework.

Official page of the conference: SCAI

Slides