Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
Open accessAbstract
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.
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Federated Learning inherently safeguards data privacy at its source.
“It inherently safeguards data privacy at its source, a crucial factor in healthcare, where data confidentiality is sacrosanct [13].”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [13]
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- Authors
- Fang, H., & Qian, Q.
- Affiliations
- Shanghai University (CN)
- Corresponding author
- Qian, Quan
- ORCID
- Qian, Quan
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2021-04-08
- Volume
- 13
- Issue
- 4
- Pages
- 94
- ISSN
- 1999-5903 (ISSN-L)
- OpenAlex
- W3140753276
- Cited by
- 424
- References
- 52
- Citations by year
- 2026: 48 · 2025: 141 · 2024: 93 · 2023: 89 · 2022: 45 · 2021: 8
- FWCI
- 20.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Internet (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2018YFB0704400)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving machine learning, homomorphic encryption, partially homomorphic encryption, federated learning, Paillier cryptosystem, computational overhead, key length
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