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Cited by 76Privacy-Preserving Technologies in DataPrivacy, Security, and Data ProtectionContext-Aware Activity Recognition Systems

Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

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Malekzadeh, Mohammad, Clegg, Richard G., Haddadi, Hamed·2018·2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)
DOI 10.1109/iotdi.2018.00025
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Abstract

An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches. In this paper, we propose a privacy-preserving sensing framework for managing access to time-series data in order to provide utility while protecting individuals' privacy. We introduce Replacement AutoEncoder, a novel feature-learning algorithm which learns how to transform discriminative features of multi-variate time-series that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users' privacy. This efficiency is achieved by defining a user-customized objective function for deep autoencoders. Replacement will not only eliminate the possibility of recognition sensitive inferences, it also eliminates the possibility of detecting the occurrence of them, that is the main weakness of other approaches such as filtering or randomization. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. Finally, we utilize the GANs for detecting the occurrence of replacement, after releasing data, and show that this can be done only if the adversarial network is trained on the users' original data.

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  • The replacement autoencoder can retain recognition accuracy of non-sensitive tasks using state-of-the-art techniques while reducing detection capability for sensitive tasks.

    “The result shows that the proposed replacement autoencoder can retain the recognition accuracy of non-sensitive tasks using state-of-the-art techniques while simultaneously reducing detection capability for sensitive tasks.”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [121]

  • Malekzadeh et al.'s replacement autoencoder removes prominent features of sensitive activities such as drinking, smoking, or using the restroom.

    “Malekzadeh et al. developed a novel replacement autoencoder that removes prominent features of sensitive activities, such as drinking, smoking, or using the restroom [121].”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [121]

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Research works printed but not linked: 15

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Authors
Malekzadeh, M., Clegg, R. G., & Haddadi, H.
Affiliations
  • Queen Mary University of London (GB)
  • Imperial College London (GB)
ORCID
Venue
2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)
Publisher
IEEE
Published
2018-04
Pages
165-176
DOI
10.1109/iotdi.2018.00025
OpenAlex
W2767028605
Cited by
76
References
32
Citations by year
2025: 6 · 2024: 11 · 2023: 9 · 2022: 10 · 2021: 7 · 2020: 12 · 2019: 10 · 2018: 10 · 2017: 1
FWCI
3.88
Type
conference paper
Language
en
Open access
Locations
  • IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
  • arXiv (Cornell University)
Funders
Engineering and Physical Sciences Research Council (EP/N028260/1, EP/N028260/2)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
privacy-preserving sensing, activity recognition, wearable sensors, Internet of Things, representation learning, generative adversarial networks, privacy leakage detection, multi-sensor data

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