{"id":1517,"date":"2020-03-12T10:24:04","date_gmt":"2020-03-12T09:24:04","guid":{"rendered":"https:\/\/www.servicemeister.org\/?p=1517"},"modified":"2020-03-31T12:10:09","modified_gmt":"2020-03-31T10:10:09","slug":"differential-privacy-eine-rechnung-die-aufgeht","status":"publish","type":"post","link":"https:\/\/www.servicemeister.org\/en\/2020\/03\/12\/differential-privacy-eine-rechnung-die-aufgeht\/","title":{"rendered":"Differential Privacy: a method that works"},"content":{"rendered":"\n

An article by Nils Klute, project manager for IoT at eco \u2013 Association of the Internet Industry<\/em><\/strong>.<\/p>\n\n\n\n

Maintaining machines and healing people \u2013 AI makes it possible. But what is often missing in this country is the database to train intelligent algorithms. Whether it be for industrial services or a cancer diagnosis, a certain mathematical process should resolve the increasing demand for data exchange and data protection.<\/em><\/p>\n\n\n\n

Europe has a problem with Big Data. In this country, industry and science are lacking the basic information that is necessary for training artificial intelligent (AI) algorithms. Algorithms like the ones that help doctors heal people. In January 2020, \u00c4rzteblatt<\/a> quoted German Health Minister Jens Spahn speaking at the DLD Conference<\/a>: \u201cIf thousands of patients with diabetes, cancer or dementia made their data available, we could learn from them.\u201d <\/p>\n\n\n\n

Market leanings and the threat of\ndata monopolies <\/strong><\/p>\n\n\n\n

The legitimate protection of\npersonal data is hindering doctors in getting what they want. Here in Germany,\nhealth information especially is regarded as being extremely sensitive and\ntherefore particularly worthy of protection. The situation is different in the\nUSA and Asia. There, platforms such as Google, Amazon, and Alibaba collect and\naggregate massive amounts of information on their users. The protection of\npersonal data, such as that regulated by the General Data Protection Regulation\n(GDPR), is of secondary importance overseas. Consequently, markets are leaning\ntowards large platform operators. According to a 2019 study by the German\nEconomic Institute<\/a>, data\nmonopolies threaten to slow down competition and innovation. <\/p>\n\n\n\n

Exchanging industry data and\ntraining algorithms<\/strong><\/p>\n\n\n\n

The situation in industry is no\ndifferent. Anyone who wants to optimize delivery networks, maintenance services\nor manufacturing processes with AI needs a lot of data. The new EU digital\nstrategy is designed to provide industry with the solution. The EU Commission\npresented its idea at the beginning of 2020: information should be easier to\nexchange via data pools in order to be able to train AI algorithms faster. At\nthe same time, local companies should become more independent of large\nplatforms. After all, \u201cThe more data we have, the smarter our algorithms will\nbe,\u201d EU Commission President Ursula von der Leyen told Tagesschau.de<\/a>. <\/p>\n\n\n\n

This is an idea that is by no means\nnew to industry, as demonstrated by the Fraunhofer-Gesellschaft’s<\/em> International Data Spaces\ninitiative<\/a>, which was\nlaunched as a research project in 2015. Additionally, according to a study by\nPricewaterhouseCoopers<\/a> from 2017,\n74 percent of all medium-sized and large companies in Germany already share\ndata with customers, suppliers, and business partners. \u201cWith this development,\nhowever, the economy is facing a dilemma,\u201d said Harald Kayser, Chief Digital\nOfficer on pwc.de<\/a>. \u201cOn the one hand, the exchange of\ndata is essential if companies want to digitize their own business model and\ntap into new revenue sources such as smart services. On the other hand, no one\nwants to risk losing control over sensitive corporate data.\u201d<\/p>\n\n\n\n

Imprecise AI prognoses and\nunexploited value creation potentials<\/strong><\/p>\n\n\n\n

No matter whether it be a loss of\ncontrol, manufacturer interests or confidentiality, whatever slows down\nindustrial information flows eventually leaves behind untapped opportunities.\nFor example, industry collects usage, maintenance, and quality data in\ndifferent IT systems for local evaluation. However, data that records system\nstatuses across several plants and different users is usually not included.\nThis results in imprecise AI prognoses and unexploited value creation\npotentials. The solution to the dilemma could now be provided by an\nas-yet-unused method: Differential Privacy.<\/p>\n\n\n\n

Using mathematics, Differential Privacy<\/a> resolves the increasing demand for\ndata exchange and data protection. This can work for machine and personal data\nalike, because the method allows Big Data collections to be specifically\nanonymized without losing statistical validity. Differential Privacy also\ngroups and aggregates information and thus conceals individual characteristics\nrelating to people, systems, and devices. If sensitive data remains secure in\nthis way, information can be exchanged while protecting the interests of\nindividuals and manufacturers.<\/p>\n\n\n\n

As reported by Tagesspiegel.de<\/a>, the first German project to\ninvestigate the possibilities of Differential Privacy was launched at the\nbeginning of 2020. The Technical University of Munich is developing a software\nand investigating the technical, economic, and political effects of the method.\nThe most important key requirement is that under no circumstances should it be\npossible to undo the anonymization. <\/p>\n\n\n\n

Use AI to its fullest extent and\nbenefit the economy<\/strong>\n\nBreaking down data silos, accelerating cancer\ntherapies, protecting the interests of industry and society alike and advancing\nthe development of AI applications in Germany and Europe; Differential Privacy\ncan be the key to the solution. A current study by the eco\nAssociation<\/a>\nshows that it\u2019s worth it: if AI technologies are used across industries to\ntheir full extent, a total economic potential of around 488 billion Euros can\nbe released for Germany alone.\n\n\n\n<\/p>\n\n\n\n

Stay up to date with our newsletter about AI and the Project Service Meister.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

An article by Nils Klute, project manager for IoT at eco \u2013 Association of the Internet Industry. Maintaining machines and healing people \u2013 AI makes it possible. But what is often missing in this country is the database to train intelligent algorithms. Whether it be for industrial services or a…Read More<\/a><\/p>\n","protected":false},"author":3,"featured_media":1519,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[16,22],"yoast_head":"\nDifferential Privacy: a method that works - Service-Meister<\/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.servicemeister.org\/en\/2020\/03\/12\/differential-privacy-eine-rechnung-die-aufgeht\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Differential Privacy: a method that works - Service-Meister\" \/>\n<meta property=\"og:description\" content=\"An article by Nils Klute, project manager for IoT at eco \u2013 Association of the Internet Industry. 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