SoK: Descriptive Statistics Under Local Differential Privacy

René Raab; Pascal Berrang; Paul Gerhart; Dominique Schröder

Paper 2024/1464

SoK: Descriptive Statistics Under Local Differential Privacy

René Raab

, Friedrich-Alexander-Universität Erlangen-Nürnberg

Pascal Berrang

, University of Birmingham

Paul Gerhart

, TU Wien

Dominique Schröder

, TU Wien, Friedrich-Alexander-Universität Erlangen-Nürnberg

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.

Note: Fixing author affiliation.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Published elsewhere. PETS 2025
Keywords: local differential privacy descriptive statistics data analysis
Contact author(s): rene raab @ fau de
History: 2024-10-21: revised; 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}
}