Kristin Lauter discusses topics such as homomorphic encryption, standardizing cryptographic protocols, machine learning on encrypted data, and attacking post-quantum cryptography with AI. She also explores the balance between privacy and data sharing in AI systems, the use of super singular isogeny graphs in cryptographic protocols, and the breakthrough of evaluating deep neural networks on homomorphically encrypted data. Additionally, she touches on challenges with activation functions in neural networks, AI applications in encrypted data, and the intersection of AI and cryptography in transformers.
