Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Afrose, Sharmin; Yao, Danfeng; Kotevska, Olivera

doi:10.1109/PST52912.2021.9647839

Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Conference · 30 November 2021

DOI:https://doi.org/10.1109/PST52912.2021.9647839· OSTI ID:1841488

Afrose, Sharmin ^[1]; Yao, Danfeng ^[1]; ^[2]

Virginia Tech, Blacksburg, VA
ORNL

Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user’s sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.

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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:: 1841488

Country of Publication:: United States

Language:: English

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