The Relevance or Irrelevance of Weights for Confidentiality and Statistical Analyses
Open accessAbstract
Sample survey weights represent a standard part of the survey statistician's repertoire, but they remain a mystery for many of those who work with survey data. They also pose a potential challenge for confidentiality protection. Much has been written about how to use weights in statistical analyses since the basic idea of weighted estimates|weighting units inversely proportional to their probability of selection|emerged from the classic paper by Horvitz and Thompson. Very little has been written about the effect of releasing survey weights on the confidentiality of survey data. The two topics are inextricably intertwined. This paper discusses both, largely from the model-based perspective, and explains what is claimed and/or known about the issue of confidentiality protection. The paper also provides a prescription for ways to deal with weights both for analysis and for disclosure limitation.
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Disclosing weights themselves could jeopardize data confidentiality, especially when variations in weights are large, because individuals receiving extreme weights may have higher risk of being identified even if their original data are de-identified.
“Steve's main concern is that while weights are inevitable in practice, disclosing weights themselves could jeopardize data confidentiality, especially when the variations in the weights are large (see Section 3.4). This is because those individuals receiving extreme weights, either large or small, may have higher risk of being identified even if their original data are de-identified.”
From Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election (2018) · cites this paper as Fienberg (2010)
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Meng, X.-L. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1161sf · Cited by 316 · see citing works
- Authors
- Fienberg, S. E.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Fienberg, Stephen E.
- ORCID
- Fienberg, Stephen E.
- Venue
- Journal of Privacy and Confidentiality
- Published
- 2010-04-01
- Volume
- 1
- Issue
- 2
- ISSN
- 2575-8527 (ISSN-L)
- OpenAlex
- W1587894268
- Cited by
- 13
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2020: 2 · 2018: 1 · 2015: 2 · 2014: 3 · 2013: 1
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Privacy and Confidentiality (publishedVersion, cc-by-nc-nd)
- KiltHub Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (EIA9876619, IIS0131884, SES-053240)
- National Institutes of Health (R01 AG023141-01)
- U.S. Army (DAAD19-02-1-3-0389)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- survey weights, confidential information protection, disclosure limitation, model-based inference, weighted estimation, Horvitz-Thompson estimator
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