Advanced search
2 files | 1.13 MB Add to list

Secure multi-party computation for personalized human activity recognition

(2023) NEURAL PROCESSING LETTERS. 55(3). p.2127-2153
Author
Organization
Abstract
Calibrating Human Activity Recognition (HAR) models to end-users with Transfer Learning (TL) often yields significant accuracy improvements. Such TL is by design done based on very personal data collected by sensors worn close to the human body. To protect the users' privacy, we therefore introduce Secure Multi-Party Computation (MPC) protocols for personalization of HAR models, and for secure activity recognition with the personalized models. Our MPC protocols do not require the end-users to reveal their sensitive data in an unencrypted manner, nor do they require the application developer to disclose their trained model parameters or any other sensitive or proprietary information with anyone in plaintext. Through experiments on HAR benchmark datasets, we demonstrate that our privacy-preserving solution yields the same accuracy gains as TL in-the-clear, i.e. when no measures to protect privacy are in place, and that our approach is fast enough for use in practice.
Keywords
Transfer learning, Human activity recognition, Convolutional neural network, Secure multi-party computation, Cryptography, Privacy

Downloads

  • (...).pdf
    • full text (Accepted manuscript)
    • |
    • UGent only (changes to open access on 2024-09-11)
    • |
    • PDF
    • |
    • 615.59 KB
  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 514.05 KB

Citation

Please use this url to cite or link to this publication:

MLA
Melanson, David, et al. “Secure Multi-Party Computation for Personalized Human Activity Recognition.” NEURAL PROCESSING LETTERS, vol. 55, no. 3, 2023, pp. 2127–53, doi:10.1007/s11063-023-11182-8.
APA
Melanson, D., Maia, R., Kim, H.-S., Nascimento, A., & De Cock, M. (2023). Secure multi-party computation for personalized human activity recognition. NEURAL PROCESSING LETTERS, 55(3), 2127–2153. https://doi.org/10.1007/s11063-023-11182-8
Chicago author-date
Melanson, David, Ricardo Maia, Hee-Seok Kim, Anderson Nascimento, and Martine De Cock. 2023. “Secure Multi-Party Computation for Personalized Human Activity Recognition.” NEURAL PROCESSING LETTERS 55 (3): 2127–53. https://doi.org/10.1007/s11063-023-11182-8.
Chicago author-date (all authors)
Melanson, David, Ricardo Maia, Hee-Seok Kim, Anderson Nascimento, and Martine De Cock. 2023. “Secure Multi-Party Computation for Personalized Human Activity Recognition.” NEURAL PROCESSING LETTERS 55 (3): 2127–2153. doi:10.1007/s11063-023-11182-8.
Vancouver
1.
Melanson D, Maia R, Kim H-S, Nascimento A, De Cock M. Secure multi-party computation for personalized human activity recognition. NEURAL PROCESSING LETTERS. 2023;55(3):2127–53.
IEEE
[1]
D. Melanson, R. Maia, H.-S. Kim, A. Nascimento, and M. De Cock, “Secure multi-party computation for personalized human activity recognition,” NEURAL PROCESSING LETTERS, vol. 55, no. 3, pp. 2127–2153, 2023.
@article{01HMYSH91YY7MBT6KFQTD5EHZ7,
  abstract     = {{Calibrating Human Activity Recognition (HAR) models to end-users with Transfer Learning (TL) often yields significant accuracy improvements. Such TL is by design done based on very personal data collected by sensors worn close to the human body. To protect the users' privacy, we therefore introduce Secure Multi-Party Computation (MPC) protocols for personalization of HAR models, and for secure activity recognition with the personalized models. Our MPC protocols do not require the end-users to reveal their sensitive data in an unencrypted manner, nor do they require the application developer to disclose their trained model parameters or any other sensitive or proprietary information with anyone in plaintext. Through experiments on HAR benchmark datasets, we demonstrate that our privacy-preserving solution yields the same accuracy gains as TL in-the-clear, i.e. when no measures to protect privacy are in place, and that our approach is fast enough for use in practice.}},
  author       = {{Melanson, David and  Maia, Ricardo and  Kim, Hee-Seok and  Nascimento, Anderson and De Cock, Martine}},
  issn         = {{1370-4621}},
  journal      = {{NEURAL PROCESSING LETTERS}},
  keywords     = {{Transfer learning,Human activity recognition,Convolutional neural network,Secure multi-party computation,Cryptography,Privacy}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{2127--2153}},
  title        = {{Secure multi-party computation for personalized human activity recognition}},
  url          = {{http://doi.org/10.1007/s11063-023-11182-8}},
  volume       = {{55}},
  year         = {{2023}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: