{"id":79021,"date":"2021-08-30T13:00:00","date_gmt":"2021-08-30T11:00:00","guid":{"rendered":"https:\/\/www.hiig.de\/?p=79021"},"modified":"2023-03-28T14:03:50","modified_gmt":"2023-03-28T12:03:50","slug":"myth-ai-models-are-abstract-and-do-not-need-personal-data","status":"publish","type":"post","link":"https:\/\/www.hiig.de\/en\/myth-ai-models-are-abstract-and-do-not-need-personal-data\/","title":{"rendered":"Myth: AI Models are abstract and do not need personal data"},"content":{"rendered":"\n<p>In supervised machine learning, models are based on abstractions from training data. The models themselves, while structurally influenced by the training data, do not contain the data themselves. It therefore seems reasonable to treat data they contain as (almost) anonymous. However, this is not true. Research has shown that deanonymization is possible under certain circumstances. Therefore, the models have to be considered as partially containing personal data and data protection law has to be taken into account when developing AI models to safeguard data subjects.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Myth<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>AI Models are abstract and do not need personal data.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"324\" height=\"189\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted.png\" alt=\"\" class=\"wp-image-75375\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted.png 324w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-60x35.png 60w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-180x105.png 180w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/03\/Busted-dark-tilted-50x29.png 50w\" sizes=\"auto, (max-width: 324px) 100vw, 324px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>&nbsp;<\/p><p>AI models are an abstraction which may or may not contain personal data. Data protection law needs to be taken into account.<\/p>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Watch the talk<\/h2>\n\n\n\n<div class=\"lyte-wrapper\" style=\"width:420px;max-width:100%;margin:5px auto;\"><div class=\"lyMe\" id=\"WYL_H2vh7h69z2A\"><div id=\"lyte_H2vh7h69z2A\" data-src=\"https:\/\/www.hiig.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=%2F%2Fi.ytimg.com%2Fvi%2FH2vh7h69z2A%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\"><\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/H2vh7h69z2A\" rel=\"nofollow\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.hiig.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FH2vh7h69z2A%2F0.jpg\" alt=\"YouTube video thumbnail\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px auto;\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Material<\/h2>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table><tbody><tr><td><i class=\"fa fa-desktop\" style=\"padding: 0 20px; vertical-align: top;\"><\/i><\/td><td><a href=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Why-AI_AI-Models-are-abstract-and-do-not-need-personal-data_Christoph-Sorge_2.pptx.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><a href=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Why-AI_AI-Models-are-abstract-and-do-not-need-personal-data_Christoph-Sorge_2.pptx.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Presentation Slides<\/a><\/td><\/tr><tr><td><i class=\"fa fa-book\" style=\"padding: 0 20px; vertical-align: top;\"><\/i><\/td><td>KEY LITERATURE<br><br>Shokri, R., Stronati, M., Song, C. &amp; Shmatikov, V. (2016). <a href=\"http:\/\/www.cs.cornell.edu\/~shmat\/shmat_oak17.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Membership Inference Attacks Against Machine Learning Models. <br><\/a><br>Al-Rubaie, M. &amp; Chang, J. M. (2019). <a href=\"https:\/\/ieeexplore.ieee.org\/document\/8677282\" target=\"_blank\" rel=\"noreferrer noopener\">Privacy-Preserving Machine Learning: Threats and Solutions. EEE Security &amp; Privacy<\/a>, 17(2), 49-58. <br><br>Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F. &amp; Lin, Z. (2021). <a href=\"https:\/\/doi.org\/10.1145\/3436755\" target=\"_blank\" rel=\"noreferrer noopener\">When Machine Learning Meets Privacy: A Survey and Outlook.<\/a> ACM Computing Surveys, 54(2), 1-36. <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">About the author<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<figure class=\"wp-block-image size-large is-style-rounded\"><img loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"800\" src=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-1200x800.jpg\" alt=\"\" class=\"wp-image-79011\" srcset=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-1200x800.jpg 1200w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-800x533.jpg 800w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-60x40.jpg 60w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-768x512.jpg 768w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-180x120.jpg 180w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-600x400.jpg 600w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-50x33.jpg 50w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-540x360.jpg 540w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-1536x1024.jpg 1536w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-2048x1365.jpg 2048w, https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Prof-Sorge_61437-1320x880.jpg 1320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><figcaption> Foto: Oliver Dietze <\/figcaption><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong><strong>Christoph Sorge<\/strong><\/strong><\/p>\n\n\n\n<p>Professor, Saarland University (Chair of Legal Informatics), Saarbr\u00fccken, Germany<\/p>\n\n\n\n<p>Christoph Sorge received his PhD in computer science from Karlsruhe Institute of Technology. He then joined the NEC Laboratories Europe, Network Research Division, as a research scientist. From 2010, Christoph was an assistant professor (&#8220;Juniorprofessor&#8221;) for Network Security at the University of Paderborn. He joined Saarland University in 2014, and is now a full professor of Legal Informatics at that university. While his primary affiliation is with the Faculty of Law, he is also a co-opted professor of computer science. He is an associated member of the CISPA &#8211; Helmholtz Center for Information Security, a senior fellow of the German Research Institute for Public Administration, and a board member of the German Association for Computing in the Judiciary. His research area is the intersection of computer science and law, with a focus on data protection.<\/p>\n\n\n\n<p><i class=\"fa fa-twitter\" style=\"padding-right: 10px;\"><\/i><a href=\"https:\/\/twitter.com\/legalinf?lang=de\">@legalinf<\/a><\/p>\n<\/div>\n<\/div>\n<div class=\"shariff shariff-align-flex-start shariff-widget-align-flex-start\"><ul class=\"shariff-buttons theme-round orientation-horizontal buttonsize-medium\"><li class=\"shariff-button linkedin shariff-nocustomcolor\" style=\"background-color:#1488bf\"><a href=\"https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-models-are-abstract-and-do-not-need-personal-data%2F\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; background-color:#0077b5; color:#fff\" target=\"_blank\"><span class=\"shariff-icon\" style=\"\"><svg width=\"32px\" height=\"20px\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 27 32\"><path fill=\"#0077b5\" d=\"M6.2 11.2v17.7h-5.9v-17.7h5.9zM6.6 5.7q0 1.3-0.9 2.2t-2.4 0.9h0q-1.5 0-2.4-0.9t-0.9-2.2 0.9-2.2 2.4-0.9 2.4 0.9 0.9 2.2zM27.4 18.7v10.1h-5.9v-9.5q0-1.9-0.7-2.9t-2.3-1.1q-1.1 0-1.9 0.6t-1.2 1.5q-0.2 0.5-0.2 1.4v9.9h-5.9q0-7.1 0-11.6t0-5.3l0-0.9h5.9v2.6h0q0.4-0.6 0.7-1t1-0.9 1.6-0.8 2-0.3q3 0 4.9 2t1.9 6z\"\/><\/svg><\/span><\/a><\/li><li class=\"shariff-button bluesky shariff-nocustomcolor\" style=\"background-color:#84c4ff\"><a href=\"https:\/\/bsky.app\/intent\/compose?text=Myth%3A%20AI%20Models%20are%20abstract%20and%20do%20not%20need%20personal%20data https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-models-are-abstract-and-do-not-need-personal-data%2F  via @hiigberlin.bsky.social\" title=\"Share on Bluesky\" aria-label=\"Share on Bluesky\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; background-color:#0085ff; color:#fff\" target=\"_blank\"><span class=\"shariff-icon\" style=\"\"><svg width=\"20\" height=\"20\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 20 20\"><path class=\"st0\" d=\"M4.89,3.12c2.07,1.55,4.3,4.71,5.11,6.4.82-1.69,3.04-4.84,5.11-6.4,1.49-1.12,3.91-1.99,3.91.77,0,.55-.32,4.63-.5,5.3-.64,2.3-2.99,2.89-5.08,2.54,3.65.62,4.58,2.68,2.57,4.74-3.81,3.91-5.48-.98-5.9-2.23-.08-.23-.11-.34-.12-.25,0-.09-.04.02-.12.25-.43,1.25-2.09,6.14-5.9,2.23-2.01-2.06-1.08-4.12,2.57-4.74-2.09.36-4.44-.23-5.08-2.54-.19-.66-.5-4.74-.5-5.3,0-2.76,2.42-1.89,3.91-.77h0Z\"\/><\/svg><\/span><\/a><\/li><li class=\"shariff-button mailto shariff-nocustomcolor\" style=\"background-color:#a8a8a8\"><a href=\"mailto:?body=https%3A%2F%2Fwww.hiig.de%2Fen%2Fmyth-ai-models-are-abstract-and-do-not-need-personal-data%2F&subject=Myth%3A%20AI%20Models%20are%20abstract%20and%20do%20not%20need%20personal%20data\" title=\"Send by email\" aria-label=\"Send by email\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\"; background-color:#999; color:#fff\"><span class=\"shariff-icon\" style=\"\"><svg width=\"32px\" height=\"20px\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 32 32\"><path fill=\"#999\" d=\"M32 12.7v14.2q0 1.2-0.8 2t-2 0.9h-26.3q-1.2 0-2-0.9t-0.8-2v-14.2q0.8 0.9 1.8 1.6 6.5 4.4 8.9 6.1 1 0.8 1.6 1.2t1.7 0.9 2 0.4h0.1q0.9 0 2-0.4t1.7-0.9 1.6-1.2q3-2.2 8.9-6.1 1-0.7 1.8-1.6zM32 7.4q0 1.4-0.9 2.7t-2.2 2.2q-6.7 4.7-8.4 5.8-0.2 0.1-0.7 0.5t-1 0.7-0.9 0.6-1.1 0.5-0.9 0.2h-0.1q-0.4 0-0.9-0.2t-1.1-0.5-0.9-0.6-1-0.7-0.7-0.5q-1.6-1.1-4.7-3.2t-3.6-2.6q-1.1-0.7-2.1-2t-1-2.5q0-1.4 0.7-2.3t2.1-0.9h26.3q1.2 0 2 0.8t0.9 2z\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div>","protected":false},"excerpt":{"rendered":"<p>In supervised machine learning, models are based on abstractions from training data. The models themselves, while structurally influenced by the training data, do not contain the data themselves. It therefore seems reasonable to treat data they contain as (almost) anonymous. However, this is not true. Research has shown that deanonymization is possible under certain circumstances.&hellip;<\/p>\n","protected":false},"author":356,"featured_media":79024,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1289,1582],"tags":[686,1243,1244],"class_list":["post-79021","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-ftif-ai-and-society","tag-ki-2","tag-machine-learnng-en","tag-why-ai-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Myth: AI Models are abstract and do not need personal data &#8211; Digital Society Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.hiig.de\/en\/myth-ai-models-are-abstract-and-do-not-need-personal-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Myth: AI Models are abstract and do not need personal data &#8211; Digital Society Blog\" \/>\n<meta property=\"og:description\" content=\"In supervised machine learning, models are based on abstractions from training data. The models themselves, while structurally influenced by the training data, do not contain the data themselves. It therefore seems reasonable to treat data they contain as (almost) anonymous. However, this is not true. Research has shown that deanonymization is possible under certain circumstances.&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.hiig.de\/en\/myth-ai-models-are-abstract-and-do-not-need-personal-data\/\" \/>\n<meta property=\"og:site_name\" content=\"HIIG\" \/>\n<meta property=\"article:published_time\" content=\"2021-08-30T11:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-03-28T12:03:50+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.hiig.de\/wp-content\/uploads\/2021\/08\/Webseite-\u2013-WHY_AI_Christoph-Sorge.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"450\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Hauke Odendahl\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Hauke Odendahl\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Myth: AI Models are abstract and do not need personal data &#8211; Digital Society Blog","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.hiig.de\/en\/myth-ai-models-are-abstract-and-do-not-need-personal-data\/","og_locale":"en_US","og_type":"article","og_title":"Myth: AI Models are abstract and do not need personal data &#8211; Digital Society Blog","og_description":"In supervised machine learning, models are based on abstractions from training data. 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