  Participatory sensing is emerging as an innovative computing paradigm that
targets the ubiquity of always-connected mobile phones and their sensing
capabilities. In this context, a multitude of pioneering applications
increasingly carry out pervasive collection and dissemination of information
and environmental data, such as, traffic conditions, pollution, temperature,
etc. Participants collect and report measurements from their mobile devices and
entrust them to the cloud to be made available to applications and users.
Naturally, due to the personal information associated to the reports (e.g.,
location, movements, etc.), a number of privacy concerns need to be taken into
account prior to a large-scale deployment of these applications. Motivated by
the need for privacy protection in Participatory Sensing, this work presents
PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore
realistic architectural assumptions and a minimal set of formal requirements
aiming at protecting privacy of both data producers and consumers. We propose
two instantiations that attain privacy guarantees with provable security at
very low additional computational cost and almost no extra communication
overhead.
