Paper 2021/1346

WeStat: a Privacy-Preserving Mobile Data Usage Statistics System

Sébastien Canard, Nicolas Desmoulins, Sébastien Hallay, Adel Hamdi, and Dominique Le Hello

Abstract

The preponderance of smart devices, such as smartphones, has boosted the development and use of mobile applications (apps) in the recent years. This prevalence induces a large volume of mobile app usage data. The analysis of such information could lead to a better understanding of users' behaviours in using the apps they have installed, even more if these data can be coupled with a given context (location, time, date, sociological data...). However, mobile and apps usage data are very sensitive, and are today considered as personal. Their collection and use pose serious concerns associated with individuals' privacy. To reconcile harnessing of data and privacy of users, we investigate in this paper the possibility to conduct privacy-preserving mobile data usage statistics that will prevent any inference or re-identification risks. The key idea is for each user to encrypt their (private and sensitive) inputs before sending them to the data processor. The possibility to perform statistics on those data is then possible thanks to the use of functional encryption, a cryptographic building block permitting to perform some allowed operations over encrypted data. In this paper, we first show how it is possible to obtain such individuals' usage of their apps, which step is necessary for our use case, but can at the same time pose some security problems w.r.t. those apps. We then design our new encryption scheme, adding some fault tolerance property to a recent dynamic decentralized function encryption scheme. We finally show how we have implemented all that, and give some benchmarks.