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Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Author

Listed:
  • Haokun Fang

    (School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China
    Current address: NO.99 Shangda Road, BaoShan District, Shanghai 200444, China.)

  • Quan Qian

    (School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China
    Materials Genome Institute, Shanghai University, Shanghai 200444, China)

Abstract

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

Suggested Citation

  • Haokun Fang & Quan Qian, 2021. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning," Future Internet, MDPI, vol. 13(4), pages 1-20, April.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:4:p:94-:d:531973
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    Citations

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    Cited by:

    1. Vivek Kumar Prasad & Pronaya Bhattacharya & Darshil Maru & Sudeep Tanwar & Ashwin Verma & Arunendra Singh & Amod Kumar Tiwari & Ravi Sharma & Ahmed Alkhayyat & Florin-Emilian Țurcanu & Maria Simona Ra, 2022. "Federated Learning for the Internet-of-Medical-Things: A Survey," Mathematics, MDPI, vol. 11(1), pages 1-47, December.
    2. Lu Han & Xiaohong Huang & Dandan Li & Yong Zhang, 2023. "RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework," Future Internet, MDPI, vol. 15(2), pages 1-20, February.
    3. Cheng, Haoyuan & Lu, Tianguang & Hao, Ran & Li, Jiamei & Ai, Qian, 2024. "Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection," Applied Energy, Elsevier, vol. 358(C).
    4. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    5. Rezak Aziz & Soumya Banerjee & Samia Bouzefrane & Thinh Le Vinh, 2023. "Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm," Future Internet, MDPI, vol. 15(9), pages 1-25, September.
    6. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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