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Paralinguistic Privacy Protection at the Edge

Published: 13 April 2023 Publication History

Abstract

Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, and well-being are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data.
In this article we introduce EDGY, a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY’s on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate, and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in “zero-shot” ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.

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cover image ACM Transactions on Privacy and Security
ACM Transactions on Privacy and Security  Volume 26, Issue 2
May 2023
335 pages
ISSN:2471-2566
EISSN:2471-2574
DOI:10.1145/3572849
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2023
Online AM: 03 November 2022
Accepted: 26 September 2022
Revised: 10 March 2022
Received: 29 May 2021
Published in TOPS Volume 26, Issue 2

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Author Tags

  1. Voice user interface
  2. Internet of Things (IoT)
  3. privacy
  4. speech analysis
  5. voice synthesis
  6. Deep Learning
  7. disentangled representation learning
  8. model optimization

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  • (2024)Privacy-Oriented Manipulation of Speaker RepresentationsIEEE Access10.1109/ACCESS.2024.340906712(82949-82971)Online publication date: 2024
  • (2024)Privacy preservation in sensor-based Human Activity Recognition through autoencoders for low-power IoT devicesInternet of Things10.1016/j.iot.2024.10118926(101189)Online publication date: Jul-2024
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