This talk will describe cryptographic technological enhancements that are ready to provide scientific researchers with mechanisms to do collaborative analytics over their datasets while keeping those datasets protected and confidential. Secure multi-party computation (MPC) is a cryptographic technology that allows independent organizations to compute an analytic jointly over their data in such a manner that nobody learns anything other than the desired output. Hence, MPC empowers organizations to make their data available for collective data aggregation and analysis while still adhering to pre-existing confidentiality constraints, legal restrictions, or corporate policies governing data sharing. Our new Conclave framework can connect to many existing backend stacks where the data already live, can automatically analyze a query to identify when a computation must cross data silos, and can leverage MPC in a scalable and usable manner when it is necessary to enable the computation.
In summary, while data sharing cyberinfrastructures today are intended to allow everyone to benefit from the initial cost of having one researcher collect data, privacy concerns (and the resulting breakdown of data sharing) transform this burden into a marginal cost that every researcher who wants access to the data must pay. We will describe how a holistic integration of secure MPC into a scientific computing infrastructure addresses a growing need in research computing: enabling scientific workflows involving collaborative experiments or replication/extension of existing results when the underlying data are encumbered by privacy constraints.
About Trusted CI: Trusted CI is the NSF Cybersecurity Center of Excellence. See our website trustedci.org.
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