Statistically Valid Inferences from Privacy-Protected Data

• Published in: The American political science review Vol. 117; no. 4; pp. 1275 - 1290
• Main Authors: EVANS, GEORGINA, KING, GARY, SCHWENZFEIER, MARGARET, THAKURTA, ABHRADEEP
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.11.2023
• Subjects: Algorithms; Computer science; Data; Datasets; Error of Measurement; Government (Administrative Body); Guarantees; Inferences; Information sharing; Mathematics; Personal information; Political science; Politics; Privacy; Public good; Public Opinion; Research subjects; Researchers; Scientists; Social sciences; Social scientists; Statistical Inference; Statistical methods; Uncertainty; Validity; United States > US
• ISSN: 0003-0554; 1537-5943
• DOI: 10.1017/S0003055422001411

Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of privacy concerns. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for research subjects, and statistical validity guarantees for researchers seeking social science insights. We build on the standard of “differential privacy,” correct for biases induced by the privacy-preserving procedures, provide a proper accounting of uncertainty, and impose minimal constraints on the choice of statistical methods and quantities estimated. We illustrate by replicating key analyses from two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting privacy. Our approach is simple to use and computationally efficient; we also offer open-source software that implements all our methods.