著者

Sachiko Kanamori Taeko Abe Takuma Ito Keita Emura Lihua Wang Shuntaro Yamamoto Le Trieu Phong Kaien Abe Sangwook Kim Ryo Nojima Seiichi Ozawa Shiho Moriai

出版者

情報処理学会

雑誌

情報処理学会論文誌 (ISSN:18827764)

巻号頁・発行日

vol.63, no.12, 2022-12-15

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To tackle financial crimes including fraudulent financial transactions (FFTs), money laundering, illegal money transfers, and bank transfer scams, several attempts have been considered to employ artificial intelligence (AI)-based FFT detection systems, particularly, deep learning-based ones. However, to the best of our knowledge, no federated learning systems using real transaction data among financial institutions have been implemented so far. This is because there are several issues to be addressed as follows: (1) it is difficult to prepare sufficient amount of transaction data for training by one financial institution (e.g., a local bank), and a small amount of dataset does not promise high accuracy for detection, (2) each transaction data contains personal information, and thus it is restricted by Act on the Protection of Personal Information in Japan to provide the transaction data to a third party. In this paper, we introduce out demonstration experimental results of privacy-preserving federated learning with five banks in Japan: the Chiba Bank, Ltd., MUFG Bank, Ltd., the Chugoku Bank, Ltd., Sumitomo Mitsui Trust Bank, Ltd., and the Iyo Bank, Ltd. As the underlying cryptographic tool, we proposed a privacy-preserving federated learning protocol which we call DeepProtect, for detecting fraudulent financial transactions. Briefly, DeepProtect allows parties to execute the stochastic gradient descent algorithm using a set of techniques for the privacy-preserving deep learning algorithms (IEEE TIFS 2018, 2019). In the demonstration experiments, we built machine learning models for detecting two types of financial frauds ― detecting fraudulent transactions in customers/victims' accounts and detecting criminals' bank accounts. We show that our federated learning system detected FFTs that could not be detected by the model built using the dataset from a single bank and detected criminals' bank accounts before the bank actually froze them.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.30(2022) (online)DOI http://dx.doi.org/10.2197/ipsjjip.30.789------------------------------

著者

王 立華 WANG Licheng CAO Zhenfu 満保 雅浩 SHAO Jun 青野 良範 BOYEN Xavier LE Trieu Phong 田中 秀磨 早稲田 篤志 野島 良 盛合 志帆

出版者

独立行政法人情報通信研究機構

雑誌

基盤研究(C)

巻号頁・発行日

2011

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従来の公開鍵暗号システムは量子計算機の発展に伴い安全性が揺らぎつつあるため、Lattice暗号と非可換暗号の研究によって、量子攻撃に耐えられる新しい暗号の構築を目指している。一方、クラウドコンピューティングというネットワーク環境が発展するにつれて、利便性が要求されると同時に、安全面やプライバシー保護への需要も高まってくる。そこで、この需要に応じる代理再暗号(PRE)や準同型暗号など暗号プリミティブとLattice、Braidなど非可換代数構造のプラットフォームを結合して、量子攻撃に耐えられ、クラウドなど新な応用環境に適応する長期利用可能な新しい暗号方式を設計することが本課題の目的とする。