Extended Capabilities for a Privacy-Enhanced Participatory Sensing Infrastructure (PEPSI)

TL;DR

This paper introduces PEPSI, a privacy-enhanced infrastructure for participatory sensing that ensures privacy for users and data consumers with minimal additional computational and communication costs.

Contribution

The work presents a novel participatory sensing architecture with formal privacy guarantees and two instantiations that are secure, efficient, and practical.

Findings

Achieves privacy with provable security guarantees.

Maintains low computational and communication overhead.

Supports large-scale participatory sensing deployments.

Abstract

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.