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MPC-Friendly Commitments for Publicly Verifiable Covert Security
We address the problem of efficiently verifying a commitment in a two-party computation.
Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation
Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis.
Human-Computer Interaction
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts.
