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雷达卡
下一代数据共享
Using Privacy Enhancing Techniques to unlock new value
利用隐私增强技术释放新价值
The value of data is often greater than the sum of its parts. In the financial services sector specifically, the use of data allows financial institutions to offer greater value and personalized services to clients and address business challenges such as fraud. However, the use of data raises privacy and security concerns from customers, institutions, and regulators—these competing obligations have historically prevented institutions from unlocking the full value of their data. Now, emerging privacy enhancing techniques (PETs) have the potential to fundamentally alter these dynamics by reducing or eliminating the privacy risks of sharing data and opening the opportunities to create value.
数据的价值往往大于其各部分的总和。特别是在金融服务业，数据的使用使金融机构能够向客户提供更高价值和个性化的服务，并应对欺诈等业务挑战。然而，数据的使用引起了客户、机构和监管机构的隐私和安全问题这些相互竞争的义务在历史上阻止了机构释放其数据的全部价值。现在，新兴的隐私增强技术（PETs）有可能通过减少或消除共享数据的隐私风险，并打开创造价值的机会，从根本上改变这些动态。
The World Economic Forum (Forum) and Deloitte Global’s latest report discusses five PETs that allow institutions, customers, and regulators to analyze and share insights from data without distributing the underlying data itself. These techniques are:
世界经济论坛（Forum）和德勤全球（Deloitte Global）的最新报告讨论了五个宠物，它们允许机构、客户和监管机构在不分发基础数据的情况下分析和分享数据中的见解。这些技术包括：
Differential privacy, where noise is added to an analytical system so that it is impossible to reverse-engineer the individual inputs
差异隐私，即噪声被添加到分析系统中，因此不可能对单个输入进行反向工程
Federated analysis, where parties share the insights from their analysis without sharing the data itself
联合分析，即当事方在不共享数据本身的情况下共享其分析的见解
Homomorphic encryption, where data is encrypted before it is shared, such that it can still be analyzed but not decoded into the original information
同态加密，即在数据共享之前对其进行加密，这样它仍然可以被分析，但不能被解码为原始信息
Zero-knowledge proofs, where users can prove their knowledge of a value without revealing the value itself
零知识证明，用户可以证明自己对某一价值的认识，而不必揭示价值本身
Secure multiparty computation, where data analysis is spread across multiple parties such that no individual party can see the complete set of inputs
安全的多方计算，其中数据分析分布在多个参与方之间，这样任何一方都看不到完整的输入集
The report outlines how each technique works at a high level and illustrates, through hypothetical use cases, how PETs can enhance privacy in financial services. Ultimately, the report makes the case that PETs can redefine the dynamics of data-sharing, allowing institutions to create value while addressing their most pressing problems in a way that is acceptable to customers, regulators, and society at large.
该报告概述了每种技术在高层次上的工作原理，并通过假设的用例说明了宠物如何增强金融服务中的隐私。最终，报告提出了这样一个观点：宠物可以重新定义数据共享的动态，允许机构在创造价值的同时，以客户、监管机构和整个社会都能接受的方式解决最紧迫的问题。
全文：
WEF_Next_Gen_Data_Sharinging_Financial_Services.pdf
(1.38 MB, 需要: 15 个论坛币)
执行摘要：
gx-fsi-executive-summary-data-sharing-2019.pdf
(2.31 MB, 需要: 5 个论坛币)
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