Efficient Homomorphic

Efficient homomorphic computation aims to perform calculations directly on encrypted data, preserving privacy while outsourcing computations to untrusted servers. Current research focuses on optimizing homomorphic operations for various machine learning models, including convolutional neural networks and simpler architectures like Weightless Neural Networks, and improving efficiency through techniques like ciphertext sharding and optimized matrix encoding. This field is crucial for enabling secure and private computation in diverse applications such as biometric identification, federated learning, and secure multi-party computation, with recent advancements demonstrating practical performance improvements in speed and accuracy.