{"id":50779,"date":"2022-06-02T16:30:38","date_gmt":"2022-06-02T14:30:38","guid":{"rendered":"https:\/\/www.cib.de\/?p=50779"},"modified":"2023-11-09T13:07:53","modified_gmt":"2023-11-09T12:07:53","slug":"pseudonymization-and-anonymization-data-protection-ai","status":"publish","type":"post","link":"https:\/\/www.cib.de\/en\/pseudonymisierung-und-anonymisierung-datenschutzkonform-in-die-welt-der-ki\/","title":{"rendered":"Pseudonymization, anonymization and data protection with AI\u00a0"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"50779\" class=\"elementor elementor-50779\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-85317cc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"85317cc\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-19c964c\" data-id=\"19c964c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-068a733 elementor-widget elementor-widget-text-editor\" data-id=\"068a733\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Companies that want to develop and use artificial intelligence need suitable training data. However, these often have a personal reference and thus fall within the scope of the General Data Protection Regulation (GDPR). Nevertheless, with\u00a0<a href=\"https:\/\/www.cib.de\/en\/pop\/\" target=\"_blank\" rel=\"noopener\">CIB PoP<\/a>\u00a0it is possible to use the valuable information for AI training \u2013 thanks to pseudonymization and anonymization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2aa8fd8 elementor-widget elementor-widget-heading\" data-id=\"2aa8fd8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Data protection and progress: a contradiction?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d44ae5 elementor-widget elementor-widget-text-editor\" data-id=\"4d44ae5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Who will be successful in the future global digital competition depends largely on who holds their own in the field of artificial intelligence. The leading players are currently the major U.S. corporations Alphabet (Google), Amazon, Meta (Facebook) and Microsoft. But China is also making great strides. In addition to military applications, the main investment here is in AI-supported surveillance of the population. A so-called social scoring system is currently under construction, in which every citizen receives points for positive behavior while points are deducted for negative behavior. The data comes from myriad sources \u2013 including the ubiquitous video cameras equipped with facial recognition software.<\/p><p>The advances in the U.S. and China are possible not only because of technical know-how, but also because of lax data protection laws. This is different in Europe, where personal data is very comprehensively protected by the General Data Protection Regulation (GDPR). The successes of AI have therefore so far been limited to areas without personal reference. This includes, for example, the use of machine-generated data from industry. According to a\u00a0<a href=\"https:\/\/www.bitkom.org\/Presse\/Presseinformation\/Kuenstliche-Intelligenz-benoetigt-Daten\" target=\"_blank\" rel=\"noopener\">Bitkom<\/a>\u00a0survey, however, many companies would like to see more far-reaching projects: 66 percent of companies involved in AI stated that personal data must be used in order to obtain usable analysis results. <em>m\u00fcssen<\/em>, um verwertbare Analyseergebnisse zu erhalten.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f7207cb elementor-widget elementor-widget-heading\" data-id=\"f7207cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Pseudonymization and anonymization enable GDPR-compliant machine learning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-80d2126 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"80d2126\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-ec4c649\" data-id=\"ec4c649\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0f4baa9 elementor-widget elementor-widget-text-editor\" data-id=\"0f4baa9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Artificial intelligence has made its major breakthrough through the discipline of \u201c<a href=\"https:\/\/de.wikipedia.org\/wiki\/Maschinelles_Lernen\" target=\"_blank\" rel=\"noopener\">Machine Learning<\/a>\u201d (ML). Here, it is not rules but data that determine the behavior of AI. For example, if an algorithm is to be developed that recognizes cats and dogs in pictures, it is not necessary to define the distinguishing features of the animals in the form of rules. Rather, the ML algorithm analyzes large sets of example images of the two animal species. Over time, this produces a generalized model that can be used to classify images that the AI has not yet seen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-39ec6ea\" data-id=\"39ec6ea\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6815aaa elementor-widget elementor-widget-image\" data-id=\"6815aaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"756\" src=\"https:\/\/www.cib.de\/wp-content\/uploads\/2022\/06\/data_protection-1024x756.jpg\" class=\"attachment-large size-large wp-image-50997\" alt=\"\" srcset=\"https:\/\/www.cib.de\/wp-content\/uploads\/2022\/06\/data_protection-1024x756.jpg 1024w, https:\/\/www.cib.de\/wp-content\/uploads\/2022\/06\/data_protection-406x300.jpg 406w, https:\/\/www.cib.de\/wp-content\/uploads\/2022\/06\/data_protection-768x567.jpg 768w, https:\/\/www.cib.de\/wp-content\/uploads\/2022\/06\/data_protection-e1768316116614.jpg 542w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-03638bf elementor-widget elementor-widget-text-editor\" data-id=\"03638bf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>At the heart of machine learning, then, is extensive training data. On the one hand, this data must be suitable for training an ML model from an economic and technical point of view. On the other hand, however, its use must not lead to a violation of the General Data Protection Regulation. If you still want to use personal data, you basically have two options:<\/p>\n<ul>\n<li>Disguise personal reference (pseudonymization)<\/li>\n<li>Remove the personal reference (anonymization)<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce08486 elementor-widget elementor-widget-heading\" data-id=\"ce08486\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Pseudonymization: The identification of the person is made more difficult<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f9641da elementor-widget elementor-widget-text-editor\" data-id=\"f9641da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>With pseudonymization, direct identifiers such as names are replaced by pseudonyms. For example, \u201cBernhard\u201d becomes \u201cHeinrich\u201d. It is important that the assignment is unambiguous: if \u201cBernhard\u201d occurs more than once in a data record, it must be replaced by \u201cHeinrich\u201d throughout. Some applications require that the pseudonymization is reversible. This is the case if it is possible to derive the original value from the pseudonym \u2013 even if a separate key is required for this.<\/p><p>Pseudonymization does not prevent the re-identification of individuals, but merely makes corresponding inferences more difficult. Therefore, pseudonymized data is subject to the GDPR. For example, they must be deleted \u2013 just like real data \u2013 when the retention obligation expires and there are no other retention reasons.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e41555 elementor-widget elementor-widget-heading\" data-id=\"4e41555\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Anonymization: Re-identification is impossible<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9dfac0 elementor-widget elementor-widget-text-editor\" data-id=\"a9dfac0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>If you want to break free of the GDPR straitjacket, you have to resort to anonymization. This is because anonymized data does not allow any conclusions to be drawn about individuals from a technical perspective. To meet this requirement, all information that would allow re-identification must be deleted, redacted or replaced by digits, for example.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-97532fd elementor-widget elementor-widget-heading\" data-id=\"97532fd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Procedure depends on the purpose of the processing<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0ebce8f elementor-widget elementor-widget-text-editor\" data-id=\"0ebce8f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>An important component of the General Data Protection Regulation is the principle of data minimization. It states, by analogy, that personal data may only be stored and processed to the extent required by the purpose. With regard to AI projects, this means: Companies must always check in advance whether the processing purpose can be achieved with anonymized data. If this is the case and the data is still not anonymized, there is a violation of the principle. Only if the purpose cannot be achieved by anonymization may a pseudonymization be performed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5d07b1 elementor-widget elementor-widget-heading\" data-id=\"f5d07b1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">CIB PoP recognizes and deletes personal data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-febdc6e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"febdc6e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-33734f2\" data-id=\"33734f2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2e974d0 elementor-widget elementor-widget-text-editor\" data-id=\"2e974d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Of course, it is not purposeful to manually remove or replace personal data in documents or data sets \u2013 especially not in AI projects where large amounts of data are processed. For this reason, CIB has launched the&nbsp;<a href=\"https:\/\/www.iais.fraunhofer.de\/\" target=\"_blank\">Fraunhofer IAIS,&nbsp;<\/a>project in collaboration with&nbsp;<a href=\"https:\/\/www.cib.de\/en\/pop\/\" target=\"_blank\">PoP&nbsp;(Protect our Privacy)<\/a>&nbsp;Within this framework, an AI-based solution was developed that automatically detects personal data in documents and removes or pseudonymizes them.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-f8a3bc6\" data-id=\"f8a3bc6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8d553df elementor-widget elementor-widget-image\" data-id=\"8d553df\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"587\" height=\"468\" src=\"https:\/\/www.cib.de\/wp-content\/uploads\/2021\/09\/CIB-PoP-graphic.svg\" class=\"attachment-large size-large wp-image-21786\" alt=\"CIB Pop\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ce4b33 elementor-widget elementor-widget-text-editor\" data-id=\"3ce4b33\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>For image-based documents,\u00a0<a href=\"https:\/\/www.cib.de\/en\/pop\/\" target=\"_blank\" rel=\"noopener\">CIB PoP<\/a>\u00a0first recognizes the text. Then, the text contents are passed to a language model (NLP model). This has been prepared with training data and is capable of independently identifying all GDPR-relevant content. In the next step, these can optionally be anonymized or blacked out. In the latter case, every trace is actually removed from the documents. The \u201cRealistic removal\u201d function is also worth highlighting. In this case, the AI reconstructs the background. For example, the scan of a completed form becomes a blank version again.<\/p><p>has been available since June 2022 as part of the CIB document viewer\u00a0<a href=\"\/en\/doxiview\/\" target=\"_blank\" rel=\"noopener\">CIB doXiview\u00a0<\/a>and opens up numerous new possibilities for document-based processes. Among other things, the solution is suitable for using documents as training data for AI tasks such as document classification and text recognition. With CIB PoP, for example, the following AI scenarios can be implemented in a GDPR-compliant manner:<\/p><ul><li>Identification of business processes based on document content<\/li><li>Extraction of process data from documents (e.g. invoices)<\/li><li>Completeness check of incoming forms, application documents and scans<\/li><\/ul><p>By the way, the research project has shown that tasks of this kind can be handled very well with anonymization. There is usually no need for an absolutely realistic replacement of the texts. In some cases, it is even sufficient to remove the texts.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b76127 elementor-widget elementor-widget-heading\" data-id=\"5b76127\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion: CIB PoP opens up new opportunities for medium-sized businesses<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-888c5c7 elementor-widget elementor-widget-text-editor\" data-id=\"888c5c7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Especially for medium-sized companies, it has been difficult, if not impossible, to use document content for training AI applications due to the high GDPR hurdles. With <a href=\"https:\/\/www.cib.de\/en\/pop\/\">CIB PoP<\/a> this is now changing. Because now it is possible in a very simple way to remove the original personal reference from a document and use the remaining content for secure, GDPR-compliant AI development.<\/p><p>Want to learn more about the technology?\u00a0<a href=\"https:\/\/www.cib.de\/en\/contact\/\" target=\"_blank\" rel=\"noopener\">Talk with us!<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Unternehmen, die k\u00fcnstliche Intelligenz entwickeln und nutzen m\u00f6chten, ben\u00f6tigen geeignete Trainingsdaten f\u00fcr die individuellen Anforderungen je nach Branche und Anwendungsfall. Oftmals haben diese jedoch einen Personenbezug und fallen damit in den Anwendungsbereich der Datenschutzgrundverordnung (DSGVO). Dennoch ist es m\u00f6glich, die wertvollen Informationen f\u00fcr das KI-Training heranzuziehen &#8211; dank Pseudonymisierung und Anonymisierung &#8211; und\u00a0CIB PoP\u00a0macht das &#8230; <a title=\"Pseudonymization, anonymization and data protection with AI\u00a0\" class=\"read-more\" href=\"https:\/\/www.cib.de\/en\/pseudonymisierung-und-anonymisierung-datenschutzkonform-in-die-welt-der-ki\/\" aria-label=\"Read more about Pseudonymisierung und Anonymisierung: datenschutzkonform in die Welt der KI\u00a0\">Read more<\/a><\/p>","protected":false},"author":237,"featured_media":50801,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[922],"tags":[1070,1096,1826,2206,2292],"class_list":["post-50779","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ki-innovation","tag-fe","tag-cib-loesung","tag-ki","tag-only-en","tag-cib-pop"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pseudonymisierung und Anonymisierung: datenschutzkonform mit KI<\/title>\n<meta name=\"description\" content=\"Datenschutz und Fortschritt: ein Widerspruch? 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