政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142099

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142099

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政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/142099

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題名:	聯邦學習與區塊鏈隱私保護在信用風險預測中的應用 Application of Federated Learning and Blockchain Privacy Protection in Credit Risk Prediction
作者:	林和勲 Lin, Ho-Hsun
貢獻者:	陳恭林和勲 Lin, Ho-Hsun
關鍵詞:	區塊鏈機器學習聯邦學習分散式身分識別可驗證憑證 Blockchain Machine learning Federated learning Decentralized identifiers Verifiable credentials
日期:	2022
上傳時間:	2022-10-05 09:09:03 (UTC+8)
摘要:	聯邦學習是一個分散式機器學習的概念，擁有資料集的參與者可以進行模型訓練，藉由提供模型訓練參數，解決訓練資料不足、資料隱私問題。區塊鏈是一種實現價值轉移的去中心化分散式資料庫技術，藉由去中心身分識別機制，允許參與者保護隱私權，保障資料自主權。隨著全球市場供給的轉變，金融機構加快銀行業務數位轉型，加強跨單位與多源異構資料整合，減少既有組織的資料孤島。然而國內個人資料保護法與各國監理單位隱私保護的重視，如何妥善應用資料並兼具合規與安全性，成了影響新興科技導入的重點。銀行是相對保守的金融機構，持有的資料集比較敏感，不能輕易地使用這些資料進行資料採擷，前提是要保證資料集使用的合法性，安全性和規範性。為了更精確地了解客戶(KYC)、客戶盡職調查(CDD)與打擊洗錢(AML)，需要巨量外部多維度的「開放資料」來優化模型，以實現風險預警與客戶管理等目標。很多時候金融機構只有聯徵中心的信用資料，資料來源包括銀行以及政府，包含經濟部中小企業處的融資服務平台和財政部的資訊中心，主要是授信資料、包含信用卡資料和客戶的個人資料。透過開放銀行及API，結合第三方服務業者共享資料，可以提供更多元的加值金融服務。. 本研究給出了一個使用企業金融授信場景的概念驗證(PoC)，使用區塊鏈框架Hyperledger Aries和隱私保護聯邦學習(FL)平台的開源專案OpenMined，並基於新興的去中心化標識符(DID)，實現使參與組織能夠相互驗證由監理機構發布的數字身分證明及憑證。所提出的分散式身分驗證機制可以應用於監管任何工作流程(Workflow)、資料收集和模型訓練，而不僅限於金融授信領域。 Federated learning is a concept of decentralized machine learning. Participants with datasets can conduct model training. By providing model training parameters, to solve the problems of insufficient training data and data privacy. Blockchain is a decentralized database technology that realizes value transfer. It allows participants to protect privacy and data autonomy through a decentralized identification mechanism. With the change in global market supply, financial institutions accelerate the digital transformation of banking business, strengthen cross-unit and multi-source heterogeneous data integration, reduce data silos in existing organizations. However, domestic personal data protection laws and the importance of privacy protection by supervisory agencies in various countries, how to properly use data and have both compliance and security have become the focus of influencing the introduction of emerging technologies. Banks are relatively conservative financial institutions, and the data sets they hold are relatively sensitive. Banks cannot easily use these data for data collection, provided that the legality, security and standardization of the use of data sets are guaranteed. In order to understand customers (KYC), customer due diligence (CDD) and anti-money laundering (AML) more accurately, a huge amount of external multi-dimensional 「Open Data」 is needed to optimize the model to achieve the goals of risk warning and customer management. In many cases, financial institutions only have the credit information of the JCIC. The data sources include banks and the government, including the financing service platform of the SMEA and the information center of the Ministry of Finance, mainly credit information , including credit card information and personal information of customers. Through open banking and APIs, and sharing data with third-party service providers, more value-added financial services can be provided. This thesis presents a proof-of-concept (PoC) using a corporate financial credit scenario, using the blockchain framework Hyperledger Aries and the privacy-preserving FL platform`s open source project OpenMined, and based on the emerging Decentralized Identifier (DID) to enable participation Organizations can mutually authenticate digital identities and credentials issued by supervisory agencies. The proposed decentralized authentication mechanism can be applied to supervise any workflow, data collection and model training, not limited to the field of financial credit.
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描述:	碩士國立政治大學資訊科學系碩士在職專班 104971007
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0104971007
資料類型:	thesis
DOI:	10.6814/NCCU202201609
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

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