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Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

Published: 27 October 2021 Publication History

Abstract

The abundance of data collected by sensors in Internet of Things devices and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this article, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.

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Cited By

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  • (2024)A Privacy Enforcing Framework for Data Streams on the EdgeIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.331513112:3(852-863)Online publication date: Jul-2024
  • (2023)Privacy through Diffusion: A White-listing Approach to Sensor Data AnonymizationProceedings of the 5th Workshop on CPS&IoT Security and Privacy10.1145/3605758.3623496(101-107)Online publication date: 26-Nov-2023
  • (2022)Specification and Operation of Privacy Models for Data Streams on the Edge2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)10.1109/ICFEC54809.2022.00018(78-82)Online publication date: May-2022

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  1. Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

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      Published In

      cover image ACM Transactions on Internet of Things
      ACM Transactions on Internet of Things  Volume 3, Issue 1
      February 2022
      201 pages
      EISSN:2577-6207
      DOI:10.1145/3492447
      Issue’s Table of Contents

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      Association for Computing Machinery

      New York, NY, United States

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      Publication History

      Published: 27 October 2021
      Accepted: 01 September 2021
      Revised: 01 August 2021
      Received: 01 January 2021
      Published in TIOT Volume 3, Issue 1

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      Author Tags

      1. Attribute inference attacks
      2. representation learning
      3. privacy-utility tradeoff
      4. edge computing

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      • (2024)A Privacy Enforcing Framework for Data Streams on the EdgeIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.331513112:3(852-863)Online publication date: Jul-2024
      • (2023)Privacy through Diffusion: A White-listing Approach to Sensor Data AnonymizationProceedings of the 5th Workshop on CPS&IoT Security and Privacy10.1145/3605758.3623496(101-107)Online publication date: 26-Nov-2023
      • (2022)Specification and Operation of Privacy Models for Data Streams on the Edge2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)10.1109/ICFEC54809.2022.00018(78-82)Online publication date: May-2022

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