Title

PEAR: A privacy-enabled architecture for crowdsensing

Document Type

Conference Proceeding

Publication Date

9-20-2017

Publication Title

Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017

Volume

2017-January

First Page

166

Last Page

171

Keywords

Community-based Sensing, Crowdsensing, Internet of Things, Opportunistic Sensing, Participatory Sensing, Privacy, Security, Wireless Sensor Networks

Abstract

© 2017 Association for Computing Machinery. Crowdsensing systems are providing solutions to the community in areas such as transportation, security, entertainment and the environment by citizens who use their consumer devices such as cellphones, wearables, and Internet of Things devices to collect various types of sensor data. Privacy is a major issue in these systems because the data collected can potentially reveal aspects considered private by the contributors of data. We propose the Privacy-Enabled ARchitecture (PEAR), a layered architecture aimed at protecting privacy in privacy-aware crowdsensing systems. We identify and describe the layers of the architecture. We propose and evaluate the design of MetroTrack, a crowdsensing system that is based on the proposed PEAR architecture.

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