Analyzing Data Privacy for Edge Systems

Olivera Kotevska; Jordan Johnson; A. Gilad Kusne

doi:10.1109/SMARTCOMP55677.2022.00058

Analyzing Data Privacy for Edge Systems

Olivera Kotevska, Jordan Johnson, A. Gilad Kusne

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

4 Scopus citations

Abstract

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	223-228
Number of pages	6
ISBN (Electronic)	9781665481526
DOIs	https://doi.org/10.1109/SMARTCOMP55677.2022.00058
State	Published - 2022
Event	8th IEEE International Conference on Smart Computing, SMARTCOMP 2022 - Espoo, Finland Duration: Jun 20 2022 → Jun 24 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022

Conference

Conference	8th IEEE International Conference on Smart Computing, SMARTCOMP 2022
Country/Territory	Finland
City	Espoo
Period	06/20/22 → 06/24/22

Funding

Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Bayesian
Gaussian process
IoT
algorithms
edge
privacy
streaming data

Access to Document

10.1109/SMARTCOMP55677.2022.00058

Cite this

Kotevska, O., Johnson, J., & Gilad Kusne, A. (2022). Analyzing Data Privacy for Edge Systems. In Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022 (pp. 223-228). (Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMARTCOMP55677.2022.00058

@inproceedings{d44a97fe509842bd96a60085ec64c988,

title = "Analyzing Data Privacy for Edge Systems",

abstract = "Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.",

keywords = "Bayesian, Gaussian process, IoT, algorithms, edge, privacy, streaming data",

author = "Olivera Kotevska and Jordan Johnson and {Gilad Kusne}, A.",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th IEEE International Conference on Smart Computing, SMARTCOMP 2022 ; Conference date: 20-06-2022 Through 24-06-2022",

year = "2022",

doi = "10.1109/SMARTCOMP55677.2022.00058",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022",

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Kotevska, O , Johnson, J & Gilad Kusne, A 2022, Analyzing Data Privacy for Edge Systems. in Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022. Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022, Institute of Electrical and Electronics Engineers Inc., pp. 223-228, 8th IEEE International Conference on Smart Computing, SMARTCOMP 2022, Espoo, Finland, 06/20/22. https://doi.org/10.1109/SMARTCOMP55677.2022.00058

Analyzing Data Privacy for Edge Systems. / Kotevska, Olivera ; Johnson, Jordan; Gilad Kusne, A.
Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 223-228 (Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022).

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

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N2 - Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

AB - Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

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KW - Gaussian process

KW - IoT

KW - algorithms

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DO - 10.1109/SMARTCOMP55677.2022.00058

M3 - Conference contribution

AN - SCOPUS:85136101908

T3 - Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022

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BT - Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022

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ER -

Analyzing Data Privacy for Edge Systems

Abstract

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Keywords

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