Estimation and detection under differential privacy constraints: applications to air transportation systems

Large scale monitoring and control systems are being increasingly deployed around us, from intelligent transportation systems to smart grids. In exchange of the benefits supplied by these systems, participating agents are required to continuously share data with these complex systems. This data often consists of highly privacy-sensitive information, including but not limited to location, power consumption traces or medical records, which can be used to compromise these agents’ privacy. For example, crowd-sourced road traffic prediction and routing applications, such as Waze and Google Maps, improve their accuracy by using
location data provided by smart-phones, but also continuously leak location information about the users of these devices. In the last few years, we have started to understand how to design signal processing schemes that formally preserve the privacy of data providers when some aggregate signal derived from this data must be shared. TO BE CONT’D

Faculty Supervisor:

Jerome Le Ny

Student:

Partner:

Massachusetts Institute of Technology

Discipline:

Engineering

Sector:

Education

University:

Polytechnique Montréal

Program: