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The next generation of data-sharing in financial services

Using privacy enhancing techniques to unlock new value

Privacy enhancing techniques have the potential to unlock enormous value for the financial sector—but they will do so only if senior executives and regulators have an awareness and working understanding of these mathematically and computationally complex techniques. The purpose of this paper is to provide an abstract and easy-to-grasp understanding of some of the most promising techniques emerging today and an illustration of how they might be deployed in the financial system.

The value of the whole of data is greater than its component 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.

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:

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Differential privacy, where noise is added to an analytical system so that it is impossible to reverse-engineer the individual inputs

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Federated analysis, where parties share the insights from their analysis without sharing the data itself

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Homomorphic encryption, where data is encrypted before it is shared, such that it can still be analyzed but not decoded into the original information

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Zero-knowledge proofs, where users can prove their knowledge of a value without revealing the value itself

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Secure multiparty computation, where data analysis is spread across multiple parties such that no individual party can see the complete set of inputs

The report outlines how each technique works at a high level and illustrates, through hypothetical use cases, how PETs can break the privacy/utility trade-off 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.

Read the full report to determine how PETs can help you unlock the full potential of your organizational data while protecting it.

The next generation of data-sharing in financial services

Read the report