Explore a groundbreaking framework for secure neural network inference in this USENIX Security '18 conference talk. Delve into the Gazelle system, which combines homomorphic encryption and two-party computation techniques to address privacy concerns in cloud-based machine learning. Learn about the Gazelle homomorphic encryption library, optimized homomorphic linear algebra kernels, and encryption switching protocols that enable efficient and private neural network predictions. Discover how Gazelle outperforms existing systems in online runtime and offers significant improvements over fully homomorphic approaches. Gain insights into secure computation, homomorphic encryption, and their applications in protecting both client input and server neural network privacy in prediction-as-a-service scenarios.