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凯捷:2023区块链在数据标记中的应用研究报告(中译版)(13页).pdf

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1、USING BLOCKCHAIN IN DATA TOKENIZATION 2023 Capgemini.All rights reserved.1USING BLOCKCHAIN IN DATA TOKENIZATIONUnlock business intelligence and growth through data at the edgeFINANCIAL SERVICESUSING BLOCKCHAIN IN DATA TOKENIZATION 2023 Capgemini.All rights reserved.2A NEW ERA OF DECENTRALIZED DATA I

2、N FINANCIAL SERVICESThe financial services industry has always been a data-driven business,and the speed at which data is proliferating is accelerating by the second.Effective leverage of all available enterprise data is now more difficult than ever and ever more critical to business success.Since i

3、t was popularized in 2005,big data attracted the energy and investment of IT managers,centralizing it at headquarters in data centers or in the cloud.Until recently,it seemed that more big data was a sufficient answer to“whats next?”However,given the constraints of size,bandwidth,and governance,cent

4、ralization appears to have run its course.The next generation of initiatives beyond big data will focus on edge data,which suggests that ever larger amounts of data are more efficiently stored at its point of origin and processed with computing power close to the thing or the person generating it.By

5、 2025,Gartner predicts that 75%of all enterprise-generated data will be created and processed outside a traditional centralized data center or cloud.1Edge data also presents challenges and opportunities to introduce new technologies such as AI,blockchain,encrypted searching,and distributed computing

6、.This paper looks at the increasing importance of data at the edge and the rationale for preserving and exploiting it close to where it is created and lives.In addition,methods by which financial services organizations can step up efforts to capitalize on edge data trends for efficient enterprise tr

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本文主要探讨了边缘数据在金融服务行业中的重要性,以及如何利用区块链和同态加密等新技术来利用边缘数据。主要观点包括: 1. 边缘数据是指在互联网边缘收集的数据,如移动电话、健身追踪器、汽车、建筑物、工厂机械等。到2025年,Gartner预测75%的企业生成的数据将在传统的集中式数据中心或云之外创建和处理。 2. 边缘数据的优势在于其分散性,可以利用分布式账本技术(如区块链)进行验证和保护,确保数据不可篡改。 3. 同态加密允许在加密数据上进行计算,而无需先解密,这使得数据可以在加密状态下进行处理和分析。 4. 联邦学习是一种机器学习技术,可以在多个独立的数据集上训练算法,而无需共享数据。这使得金融机构可以在不共享敏感数据的情况下,共同改进风险评估模型。 5. 利用边缘数据和同态加密等技术,金融机构可以提高数据隐私和安全性,同时改善风险评估和客户体验。
边缘数据如何改变金融服务行业? 如何在保护隐私的同时利用联邦学习? 区块链如何帮助企业实现数据去中心化?
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