The Project
Participatory Sensing combines the ubiquity of mobile phones with sensing capabilities of Wireless Sensor Networks. It targets pervasive collection of information, e.g., temperature, traffic conditions, or health-related data. As users produce measurements from their mobile devices, voluntary participation becomes essential. However, a number of privacy concerns – due to the personal information conveyed by data reports – hinder large-scale deployment of participatory sensing applications. Prior work on privacy protection, for participatory sensing, has often relayed on unrealistic assumptions and with no provably-secure guarantees.
The goal of this project is to introduce PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions
and a minimal set of (formal) privacy requirements, aiming at protecting privacy of both data producers and consumers.
We design a solution that attains privacy guarantees with
provable security at very low additional computational cost and almost no extra communication overhead.
People
Emiliano De Cristofaro, University College London (previously at UC Irvine and PARC)
Claudio Soriente, ETH Zurich (previously at Universidad Politecnica de Madrid)
Publications
- Emiliano De Cristofaro and Claudio Soriente
PEPSI: Privacy-Enhancing Participatory Sensing Infrastructure
Proceedings of ACM WiSec 2011
- Emiliano De Cristofaro and Claudio Soriente
Participatory Privacy: Enabling Privacy in Participatory Sensing
IEEE Network Magazine January-February 2013 - Emiliano De Cristofaro and Claudio Soriente
Extended Capabilities for a Privacy-Enhanced Participatory Sensing Infrastructure (PEPSI)
IEEE Transactions on Information Forensics and Security (TIFS), Vol. 8, No. 12, 2013
Related Work
We mantain a related work page with a (non-comprehensive) list of papers on security and privacy in participatory sensing.
Funding Grants and Acknowledgments
This research has been partially funded by US Intelligence Advanced Research Projects Activity
(IARPA) under grant FA8750-09-2-0071, the Madrid Regional Council – CAM
under project CLOUDS (S2009TIC-1692), the Spanish Research Agency – MICINN under project CloudStorm
(TIN2010-19077), and the European Commission under projects MASSIF
(FP7-257475) and STREAM (FP7-216181).
We are also very grateful to Nokia and RIM for donating mobile devices used in our experiments.