Several studies have shown that the generation and exploitation of human mobility traces pose privacy-related challenges. In order to protect user anonymity, some approaches have been developed, essentially for outdoor location data provided by GPS receivers or mobile network operators. With the increasing development of indoor location techniques, a new type of data is being generated. This data is finer-grained and tends to exhibit a higher sampling rate compared to outdoor tracking solutions. The information collected across multiple devices also creates new privacy risks.
The goal of this project is to evaluate the risks associated with the generation of indoor mobility traces and to propose solutions that reduces the risks of inferring user private information from indoor mobility traces. For this, the student will have to first survey existing privacy-preserving techniques. From a practical perspective, the student will learn about an existing indoor location tracking system developed by the DOPLab and apply a subset of privacy-preserving solutions to this system.