Differential privacy and social science: An urgent puzzle

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DOI:	https://doi.org/10.1162/99608f92.63a22079
URL:	https://madoc.bib.uni-mannheim.de/58666
Weitere URL:	https://hdsr.mitpress.mit.edu/pub/g9o4z8au/release...
URN:	urn:nbn:de:bsz:180-madoc-586666
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2020
Titel einer Zeitschrift oder einer Reihe:	Harvard Data Science Review : HDSR
Band/Volume:	2
Heft/Issue:	1
Seitenbereich:	1-21
Ort der Veröffentlichung:	Cambridge, MA
Verlag:	MIT Press
ISSN:	2644-2353
Verwandte URLs:	https://www.iab.de/897/section.aspx/Publ...
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Außerfakultäre Einrichtungen > MZES - Arbeitsbereich A Fakultät für Sozialwissenschaften > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-2020)
Bereits vorhandene Lizenz:	Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet:	300 Sozialwissenschaften, Soziologie, Anthropologie
Abstract:	Accessing and combining large amounts of data is important for quantitative social scientists, but increasing amounts of data also increase privacy risks. To mitigate these risks, important players in official statistics, academia, and business see a solution in the concept of differential privacy. In this opinion piece, we ask how differential privacy can benefit from social-scientific insights, and, conversely, how differential privacy is likely to transform social science. First, we put differential privacy in the larger context of social science. We argue that the discussion on implementing differential privacy has been clouded by incompatible subjective beliefs about risk, each perspective having merit for different data types. Moreover, we point out existing social-scientific insights that suggest limitations to the premises of differential privacy as a data protection approach. Second, we examine the likely consequences for social science if differential privacy is widely implemented. Clearly, workflows must change, and common social science data collection will become more costly. However, in addition to data protection, differential privacy may bring other positive side effects. These could solve some issues social scientists currently struggle with, such as p-hacking, data peeking, or overfitting; after all, differential privacy is basically a robust method to analyze data. We conclude that, in the discussion around privacy risks and data protection, a large number of disciplines must band together to solve this urgent puzzle of our time, including social science, computer science, ethics, law, and statistics, as well as public and private policy.
Zusätzliche Informationen:	Online-Ressource