Cited by 13Survey Sampling and Estimation TechniquesPrivacy-Preserving Technologies in DataSurvey Methodology and Nonresponse

The Relevance or Irrelevance of Weights for Confidentiality and Statistical Analyses

Open access
Fienberg, Stephen E.·2010·Journal of Privacy and Confidentiality

Abstract

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.

Integrity signals

Registry

Crossref · 10.29012/jpc.v1i2.572

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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.”

Authors
Fienberg, S. E.
Affiliations
Carnegie Mellon University (US)
Corresponding author
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review