Artificial intelligence (AI) depends on data. In sensitive domains – such as healthcare, security, finance, and many more – there is therefore tension between unleashing the power of AI and maintaining the confidentiality and security of the relevant data.

This book – intended for researchers in academia and R&D engineers in industry – explains how advances in three areas--AI, privacy-preserving techniques, and acceleration—allow us to achieve the dream of high performance privacy-preserving AI. It also discusses applications enabled by this emerging interplay.

The book covers techniques, specifically secure multi-party computation and homomorphic encryption, that provide complexity theoretic security guarantees even with a single data point. These techniques have traditionally been too slow for real-world usage, and the challenge is heightened with the large sizes of today's state-of-the-art neural networks, including large language models (LLMs). This book does not cover techniques like differential privacy that only concern statistical anonymization of data points.