Detecting and Explaining Privacy Risks on Temporal Data

Speaker: Marie-Christine Rousset, Professor at University of Grenoble

Abstract:

Personal data are increasingly disseminated over the Web through mobile devices and smart environments,
and are exploited for developing more and more sophisticated services and applications. All these advances
come with serious risks for privacy breaches that may reveal private information wanted to remain undisclosed
by data producers. It is therefore of utmost importance to help them to identify privacy risks raised by requests
of service providers for utility purposes.

In this talk, I will focus on the temporal aspect for privacy protection since many applications handle dynamic data
(e.g., electrical consumption, time series, mobility data) for which temporal data are considered as sensitive and
aggregates on time are important for data analytics. I will present a formal approach for detecting incompatibility
between privacy and utility queries expressed as temporal aggregate conjunctive queries. The distinguishing point
of our approach is to be data-independent and to come with an explanation based on the query expressions only.
This explanation is intended to help data producers understand the detected privacy breaches and guide their choice
of the appropriate technique to correct it.