SoK: Descriptive Statistics Under Local Differential Privacy
Raab R, Berrang P, Gerhart P, Schröder D (2025)
Publication Type: Journal article
Publication year: 2025
Journal
Book Volume: 2025
Pages Range: 118-149
Issue: 1
DOI: 10.56553/popets-2025-0008
Abstract
Local Differential Privacy (LDP) provides a formal guarantee of privacy that enables the collection and analysis of sensitive data without revealing any individual's data. While LDP methods have been extensively studied, there is a lack of a systematic and empirical comparison of LDP methods for descriptive statistics. In this paper, we first provide a systematization of LDP methods for descriptive statistics, comparing their properties and requirements. We demonstrate that several mean estimation methods based on sampling from a Bernoulli distribution are equivalent in the one-dimensional case and introduce methods for variance estimation. We then empirically compare methods for mean, variance, and frequency estimation. Finally, we provide recommendations for the use of LDP methods for descriptive statistics and discuss their limitations and open questions.
Authors with CRIS profile
René Raab
Machine Learning and Data Analytics Lab
Dominique Schröder
Lehrstuhl für Informatik 13 (Angewandte Kryptographie)
Related research project(s)
Involved external institutions
University of Birmingham
United Kingdom (GB)
Technische Universität Wien / Vienna University of Technology
Austria (AT)
United Kingdom (GB)
Technische Universität Wien / Vienna University of Technology
Austria (AT)
How to cite
APA:
Raab, R., Berrang, P., Gerhart, P., & Schröder, D. (2025). SoK: Descriptive Statistics Under Local Differential Privacy. Proceedings on Privacy Enhancing Technologies, 2025, 118-149. https://doi.org/10.56553/popets-2025-0008
MLA:
Raab, René, et al. "SoK: Descriptive Statistics Under Local Differential Privacy." Proceedings on Privacy Enhancing Technologies 2025 (2025): 118-149.
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