By Jayavanth Shenoy, Onai, USA, jayavanth@onai.com | Patrick Grinaway, Onai, USA | Shriphani Palakodety, Onai, USA

Publication Date: 09 Apr 2024

Suggested Citation: Jayavanth Shenoy, Patrick Grinaway, Shriphani Palakodety (2024), "High Performance Privacy Preserving AI", Boston-Delft: now publishers, http://dx.doi.org/10.1561/9781638283454

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Description

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.