Party Computation

Party computation, encompassing techniques like multi-party computation (MPC) and homomorphic encryption (HE), aims to enable collaborative computation on sensitive data without revealing individual inputs. Current research focuses on improving the efficiency and scalability of these methods, particularly for machine learning tasks, often employing techniques like Shamir's secret sharing and optimized protocols for specific neural network operations (e.g., convolutions, ReLU activations). This field is crucial for advancing privacy-preserving machine learning, with applications ranging from secure medical data analysis to confidential model deployment and federated learning, addressing critical concerns in data security and collaboration.