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Secure human action recognition by encrypted neural network inference

Author

Listed:
  • Miran Kim

    (Hanyang University
    Hanyang University)

  • Xiaoqian Jiang

    (University of Texas Health Science Center)

  • Kristin Lauter

    (Meta AI Research)

  • Elkhan Ismayilzada

    (Ulsan National Institute of Science and Technology)

  • Shayan Shams

    (San Jose State University)

Abstract

Advanced computer vision technology can provide near real-time home monitoring to support “aging in place” by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.

Suggested Citation

  • Miran Kim & Xiaoqian Jiang & Kristin Lauter & Elkhan Ismayilzada & Shayan Shams, 2022. "Secure human action recognition by encrypted neural network inference," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32168-5
    DOI: 10.1038/s41467-022-32168-5
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    References listed on IDEAS

    as
    1. David Froelicher & Juan R. Troncoso-Pastoriza & Jean Louis Raisaro & Michel A. Cuendet & Joao Sa Sousa & Hyunghoon Cho & Bonnie Berger & Jacques Fellay & Jean-Pierre Hubaux, 2021. "Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. David Froelicher & Juan R. Troncoso-Pastoriza & Jean Louis Raisaro & Michel A. Cuendet & Joao Sa Sousa & Hyunghoon Cho & Bonnie Berger & Jacques Fellay & Jean-Pierre Hubaux, 2021. "Author Correction: Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
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