What technology allows the secure processing of functions with private inputs from multiple parties?

Secure Multi-Party Computation (SMPC) is the correct choice because it enables multiple parties to jointly perform computations over their private inputs without revealing those inputs to one another. This technology is vital in scenarios where privacy and confidentiality are paramount, such as in collaborative data analysis or joint decision-making without disclosing sensitive information.

SMPC works by utilizing cryptographic techniques that ensure that each party can contribute their data to the computation process in a manner that prevents any of the participants from seeing the other parties' raw data. The outcome of the computation is made available to all parties, but the individual inputs remain confidential. This capability is invaluable in sectors like finance, healthcare, or any environment where the collaboration between parties is needed without compromising individual data privacy.

The other options do not specifically meet the criteria of securely processing functions with private inputs from multiple parties in the same way. Peer-to-Peer Networking involves direct communication between participants but does not inherently protect the privacy of the data being shared. Federated Learning allows models to learn from data that remains on users' devices without sharing raw data but primarily focuses on machine learning and model training rather than general secure computation. Data Masking, while useful for obscuring sensitive details in datasets, does not provide a mechanism for