Paper 2024/1464
SoK: Descriptive Statistics Under Local Differential Privacy
, Friedrich-Alexander-Universität Erlangen-Nürnberg, University of Birmingham, TU Wien, TU Wien, Friedrich-Alexander-Universität Erlangen-NürnbergAbstract
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.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. PETS 2025
- Keywords
- local differential privacydescriptive statisticsdata analysis
- Contact author(s)
- rene raab @ fau de
- History
- 2024-09-21: approved
- 2024-09-19: received
- See all versions
- Short URL
- https://ia.cr/2024/1464
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/1464,
author = {René Raab and Pascal Berrang and Paul Gerhart and Dominique Schröder},
title = {{SoK}: Descriptive Statistics Under Local Differential Privacy},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/1464},
year = {2024},
url = {https://eprint.iacr.org/2024/1464}
}
