Multiparty computation as supplementary measure and potential data anonymization tool

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Privacy-enhancing technologies like secure multiparty computation, homomorphic encryption, federated learning, differential privacy, secure enclaves, zero-knowledge proof or synthetic data are becoming increasingly relevant in practice and considered by regulators.

Approaching the challenging trade-off between data privacy and data utility for a vast variety of use cases, privacy-enhancing technologies embed important privacy-by-design principles in the data life cycle. They aim at enabling increased collaborative information-sharing while mitigating the risk for privacy and security in previously unknown ways.

This is particularly true for secure multiparty computation, also known as one of the most influential achievements of modern cryptography.

Previously unimaginable, MPC allows for sharing data insights while keeping the data itself private. Two or more parties can receive an output of a computation based on their combined data without revealing their own data to the other parties. All inputs remain private. At the same time, the data remains protected by encryption while in use. The participant’s input data doesn’t need to be transferred to a central location

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