PEAR: A privacy-enabled architecture for crowdsensing

Alfredo J. Perez; Sherali Zeadally

doi:10.1145/3129676.3129685

PEAR: A privacy-enabled architecture for crowdsensing

Alfredo J. Perez, Sherali Zeadally

School of Information Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017
Pages	166-171
Number of pages	6
ISBN (Electronic)	9781450350273
DOIs	https://doi.org/10.1145/3129676.3129685
State	Published - Sep 20 2017
Event	2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017 - Krakow, Poland Duration: Sep 20 2017 → Sep 23 2017

Publication series

Name	Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017
Volume	2017-January

Conference

Conference	2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017
Country/Territory	Poland
City	Krakow
Period	9/20/17 → 9/23/17

Bibliographical note

Funding Information:
Alfredo J. Perez was supported by the US National Science Foundation and the US Department of Defense’s ASSURE program under award 1560214. Sherali Zeadally’s work was supported by a University Research Professorship Award from the University of Kentucky in 2016.

Publisher Copyright:
© 2017 Association for Computing Machinery.

Keywords

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

ASJC Scopus subject areas

Computer Science (all)
Control and Systems Engineering

Access to Document

10.1145/3129676.3129685

Cite this

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title = "PEAR: A privacy-enabled architecture for crowdsensing",

abstract = "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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language = "English",

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Perez, AJ & Zeadally, S 2017, PEAR: A privacy-enabled architecture for crowdsensing. in Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017. Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017, vol. 2017-January, pp. 166-171, 2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017, Krakow, Poland, 9/20/17. https://doi.org/10.1145/3129676.3129685

PEAR : A privacy-enabled architecture for crowdsensing. / Perez, Alfredo J.; Zeadally, Sherali.

Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017. 2017. p. 166-171 (Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PEAR: A privacy-enabled architecture for crowdsensing

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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