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High Performance Privacy Preserving AI

Jayavanth Shenoy, Patrick Grinaway, Shriphani Palakodety
Onai, USA
Boston–Delft: Now Publishers, 2024

@article{shenoy2024high,

   title={High Performance Privacy Preserving AI},

   author={Shenoy, Jayavanth and Grinaway, Patrick and Palakodety, Shriphani and others},

   year={2024},

   publisher={Now Publishers, Inc.}

}

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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.
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