Peer-to-peer privacy-preserving vertical federated learning without trusted third-party coordinator-Reference-Cited by-同舟云学术

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Peer-to-peer privacy-preserving vertical federated learning without trusted third-party coordinator

Published:2023-07-12 Issue:5 Volume:16 Page:2242-2255
ISSN:1936-6442
Container-title:Peer-to-Peer Networking and Applications
language:en
Short-container-title:Peer-to-Peer Netw. Appl.

Author:

Yu Xiaopeng,Feng Jie,Zhao Wei,Yang Haomiao,Tang Dianhua

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s12083-023-01512-x.pdf

Reference47 articles.

1. Liu B, Ding M, Shaham S, Rahayu W, Lin Z (2021) When machine learning meets privacy: A survey and outlook. ACM Computing Surveys 54(2):1–36

2. Wood A, Kahrobaei D, Najarian K (2020) Homomorphic encryption for machine learning in medicine and bioinformatics. ACM Computing Surveys 53(4):1–35

3. Dennis IR Budianto, Azaria RK, Gunawan AAS (2022) Machine learning-based approach on dealing with binary classification problem in imbalanced financial data. International Seminar on Machine Learning. Optimization, and Data Science. Piscataway. IEEE pp 152–156

4. Chen C, Wu B, Wang L, Chen C, Zhang B (2020) Nebula: A scalable privacy-preserving machine learning system in ant financial. In: 29th ACM International Conference on Information and Knowledge Management pp. 3369–3372 ACM New York

5. Zhang Y, Bai G, Li X, Curtis C, Ko RKL (2020) Privcoll: Practical privacy-preserving collaborative machine learning. In: 25th European Symposium on Research in Computer Security, pp. 399–418. Springer, Heidelberg

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Homomorphic Encryption Enabled SVM for Preserving Privacy of P2P Communication;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24

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