Financial services institutions and banks around the globe face monumental challenges as they look to streamline service delivery for customer transactions, manage multi-party loan processes, collaborate on industry benchmarks and indices, and eliminate fraud and cybercrime. Historically the market has primarily relied upon manual approaches for sharing and managing transaction data. But advances in confidential computing (sometimes called CC or trusted computing), combined with federated machine learning (FML), are helping financial organizations better share data and outcomes, while alleviating many privacy and security concerns.
Before we look at some real-world use cases of how CC and FML are helping to improve security and privacy in financial services, let’s first quickly review what these technologies are. CC uses hardware memory protection (usually in a CPU) to help isolate data payloads. This represents a fundamental shift in how computation is done at the hardware level and changes how vendors can structure the application programs. It enables encrypted data to be processed in memory while decreasing the risk of exposure to the rest of the system. This reduces the potential for sensitive data to be exposed, while providing a higher degree of control and transparency for users. CC’s secret sauce relies on Trusted Execution Environments (TEEs) in the firmware (also referred to as enclaves) and can enable collaboration between a variety of parties including hardware and software vendors, cloud providers, developers, open source experts, academics and more. A good example of this sort of collaboration is the Confidential Computing Consortium.