{"id":29184,"date":"2024-04-14T22:39:56","date_gmt":"2024-04-14T19:39:56","guid":{"rendered":"https:\/\/hgpu.org\/?p=29184"},"modified":"2024-04-14T22:39:56","modified_gmt":"2024-04-14T19:39:56","slug":"high-performance-privacy-preserving-ai","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=29184","title":{"rendered":"High Performance Privacy Preserving AI"},"content":{"rendered":"<p>Artificial intelligence (AI) depends on data. In sensitive domains &#8211; such as healthcare, security, finance, and many more &#8211; there is therefore tension between unleashing the power of AI and maintaining the confidentiality and security of the relevant data. This book &#8211; intended for researchers in academia and R&amp;D engineers in industry &#8211; explains how advances in three areas &#8211; AI, privacy-preserving techniques, and acceleration\u2014allow 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&#8217;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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence (AI) depends on data. In sensitive domains &#8211; such as healthcare, security, finance, and many more &#8211; there is therefore tension between unleashing the power of AI and maintaining the confidentiality and security of the relevant data. This book &#8211; intended for researchers in academia and R&amp;D engineers in industry &#8211; explains how [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3,287],"tags":[1733,117,105,1782,34,1800],"class_list":["post-29184","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","category-security","tag-ai","tag-artificial-intelligence","tag-book","tag-computer-science","tag-neural-networks","tag-security"],"views":1460,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29184","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=29184"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29184\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29184"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29184"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29184"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}