{ "item_title" : "High Performance Privacy Preserving AI", "item_author" : [" Jayavanth Shenoy", "Patrick Grinaway", "Shriphani Palakodety "], "item_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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/63/828/344/1638283443_b.jpg", "price_data" : { "retail_price" : "90.00", "online_price" : "90.00", "our_price" : "90.00", "club_price" : "90.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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