{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:44:13Z","timestamp":1777621453232,"version":"3.51.4"},"reference-count":56,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Big data and artificial intelligence pose a new challenge for data protection as these techniques allow predictions to be made about third parties based on the anonymous data of many people. Examples of predicted information include purchasing power, gender, age, health, sexual orientation, ethnicity, etc. The basis for such applications of \u201cpredictive analytics\u201d is the comparison between behavioral data (e.g. usage, tracking, or activity data) of the individual in question and the potentially anonymously processed data of many others using machine learning models or simpler statistical methods. The article starts by noting that predictive analytics has a significant potential to be abused, which manifests itself in the form of social inequality, discrimination, and exclusion. These potentials are not regulated by current data protection law in the EU; indeed, the use of anonymized mass data takes place in a largely unregulated space. Under the term \u201cpredictive privacy,\u201d a data protection approach is presented that counters the risks of abuse of predictive analytics. A person's predictive privacy is violated when personal information about them is predicted without their knowledge and against their will based on the data of many other people. Predictive privacy is then formulated as a protected good and improvements to data protection with regard to the regulation of predictive analytics are proposed. Finally, the article points out that the goal of data protection in the context of predictive analytics is the regulation of \u201cprediction power,\u201d which is a new manifestation of informational power asymmetry between platform companies and society.<\/jats:p>","DOI":"10.1177\/20539517231166886","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T02:15:01Z","timestamp":1681697701000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":61,"title":["Predictive privacy: Collective data protection in the context of artificial intelligence and big data"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-9919","authenticated-orcid":false,"given":"Rainer","family":"M\u00fchlhoff","sequence":"first","affiliation":[{"name":"Ethics of Artificial Intelligence, Institut f\u00fcr Kognitionswissenschaft, Universit\u00e4t Osnabr\u00fcck, Osnabr\u00fcck, Germany"}]}],"member":"179","published-online":{"date-parts":[[2023,4,16]]},"reference":[{"key":"bibr1-20539517231166886","doi-asserted-by":"crossref","unstructured":"Abadi M, Chu A, Goodfellow I, et al. (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS\u201916, pp.308\u2013318. DOI:10.1145\/2976749.2978318.","DOI":"10.1145\/2976749.2978318"},{"key":"bibr2-20539517231166886","unstructured":"Article 29 Data Protection Working Party (2007)\n                      Opinion 4\/2007 on the concept of personal data\n                      . 01248\/07\/EN WP 136. Available at: https:\/\/ec.europa.eu\/justice\/article-29\/documentation\/opinion-recommendation\/files\/2007\/wp140_en.pdf."},{"key":"bibr3-20539517231166886","unstructured":"Article 29 Data Protection Working Party (2018)\n                      Guidelines on automated individual decision-making and Profiling for the purposes of Regulation 2016\/679\n                      . 17\/EN WP251rev.01. 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