Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks-Reference-Cited by-同舟云学术

Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks

Published:2022 Issue:0 Volume:30 Page:789-795
ISSN:1882-6652
Container-title:Journal of Information Processing
language:en
Short-container-title:Journal of Information Processing

Author:

Kanamori Sachiko¹,Abe Taeko¹,Ito Takuma¹,Emura Keita¹,Wang Lihua¹,Yamamoto Shuntaro¹,Phong Le Trieu¹,Abe Kaien²,Kim Sangwook²,Nojima Ryo¹,Ozawa Seiichi²,Moriai Shiho¹

Affiliation:

1. National Institute of Information and Communications Technology

2. Kobe University

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Link

https://www.jstage.jst.go.jp/article/ipsjjip/30/0/30_789/_pdf

Reference9 articles.

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3. [3] Chen, Z., Khoa, L.D.V., Teoh, E.N., Nazir, A., Karuppiah, E.K. and Lam, K.S.: Machine learning techniques for anti-money laundering (AML)solutions in suspicious transaction detection: A review, Knowledge and Information Systems, Vol.57, No.2, pp.245-285 (2018).

4. [4] Lindell, Y.: Secure multiparty computation, Comm. ACM, Vol.64, No.1, pp.86-96 (2021).

5. [5] Pandey, K., Sachan, P., Shakti and Ganpatrao, N.G.: A Review of Credit Card Fraud Detection Techniques, 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp.1645-1653 (online), DOI: 10.1109/ICCMC51019.2021.9418024 (2021).

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