{"id":7322,"date":"2023-03-14T01:20:56","date_gmt":"2023-03-14T00:20:56","guid":{"rendered":"https:\/\/joapen.com\/blog\/?p=7322"},"modified":"2023-03-14T01:20:59","modified_gmt":"2023-03-14T00:20:59","slug":"fully-homomorphic-encryption","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2023\/03\/14\/fully-homomorphic-encryption\/","title":{"rendered":"Fully Homomorphic Encryption"},"content":{"rendered":"\n<p>This post is a summary of this video titled: <a href=\"https:\/\/www.youtube.com\/watch?v=Yr2B1OyNT6c\" target=\"_blank\" rel=\"noreferrer noopener\">Privacy Preserving Machine Learning with Fully Homomorphic Encryption<\/a>, done by <a href=\"https:\/\/www.linkedin.com\/in\/jordan-fr%C3%A9ry-782899a8\/\" target=\"_blank\" rel=\"noreferrer noopener\">Jordan Fr\u00e9ry<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p>In today&#8217;s digital age, protecting privacy has become increasingly difficult. However, new developments such as Fully Homomorphic Encryption (FHE) provide a means of safeguarding sensitive client information. We are excited to present Concrete-ML, our open-source library that allows for the seamless conversion of Machine Learning (ML) models into their FHE counterparts. With our technology, clients can enjoy zero-trust interactions with service providers while also enabling the deployment of ML models on untrusted servers without compromising the privacy of user data.<\/p>\n\n\n\n<p>Concrete-ML (make the internet safe): <a href=\"https:\/\/github.com\/zama-ai\/concrete-ml\">https:\/\/github.com\/zama-ai\/concrete-ml<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A Wardley Map for my poor memory<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"583\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/03\/image-7-1024x583.png\" alt=\"\" class=\"wp-image-7323\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/03\/image-7-1024x583.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/03\/image-7-300x171.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/03\/image-7-768x437.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/03\/image-7.png 1243w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>This post is a summary of this video titled: Privacy Preserving Machine Learning with Fully Homomorphic Encryption, done by Jordan Fr\u00e9ry. Abstract In today&#8217;s digital age, protecting privacy has become increasingly difficult. However, new developments such as Fully Homomorphic Encryption (FHE) provide a means of safeguarding sensitive client information. We are excited to present Concrete-ML, &#8230; <a title=\"Fully Homomorphic Encryption\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2023\/03\/14\/fully-homomorphic-encryption\/\" aria-label=\"Read more about Fully Homomorphic Encryption\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[151,204],"tags":[],"class_list":["post-7322","post","type-post","status-publish","format-standard","hentry","category-machine-learning","category-wardley-maps"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Fully Homomorphic Encryption -<\/title>\n<meta name=\"description\" content=\"This post is a summary of this video titled: Privacy Preserving Machine Learning with Fully Homomorphic Encryption, done by Jordan Fr\u00e9ry. Abstract In - joapen projects\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/joapen.com\/blog\/2023\/03\/14\/fully-homomorphic-encryption\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fully Homomorphic Encryption -\" \/>\n<meta property=\"og:description\" content=\"This post is a summary of this video titled: Privacy Preserving Machine Learning with Fully Homomorphic Encryption, done by Jordan Fr\u00e9ry. 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