Analyzing Data Privacy for Edge Systems

Kotevska, Olivera; Johnson, Jordan; Kusne, Aaron Gilad

doi:10.1109/SMARTCOMP55677.2022.00058

Analyzing Data Privacy for Edge Systems

Conference · Wed Jun 01 00:00:00 EDT 2022

DOI:https://doi.org/10.1109/SMARTCOMP55677.2022.00058· OSTI ID:1878699

^[1]; Johnson, Jordan ^[1]; Kusne, Aaron Gilad ^[2]

ORNL
National Institute of Standards and Technol/University of Maryland, College Park

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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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1878699

Country of Publication:: United States

Language:: English

References (15)

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