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The Complexities of Differential Privacy for Survey Data

In: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences

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  • Jörg Drechsler
  • James Bailie

Abstract

The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2020 Decennial Census. However, despite its attractive theoretical properties, implementing DP in practice remains challenging, especially when it comes to survey data. In this paper we present some results from an ongoing project funded by the U.S. Census Bureau that is exploring the possibilities and limitations of DP for survey data. Specifically, we identify five aspects that need to be considered when adopting DP in the survey context: the multi-staged nature of data production; the limited privacy amplification from complex sampling designs; the implications of survey-weighted estimates; the weighting adjustments for nonresponse and other data deficiencies, and the imputation of missing values. We summarize the project’s key findings with respect to each of these aspects and also discuss some of the challenges that still need to be addressed before DP could become the new data protection standard at statistical agencies.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jörg Drechsler & James Bailie, 2024. "The Complexities of Differential Privacy for Survey Data," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:15024
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    References listed on IDEAS

    as
    1. Jörg Drechsler, 2023. "Differential Privacy for Government Agencies—Are We There Yet?," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(541), pages 761-773, January.
    2. John M. Abowd & Ian M. Schmutte, 2019. "An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices," American Economic Review, American Economic Association, vol. 109(1), pages 171-202, January.
    3. Marco Avella-Medina, 2021. "Privacy-Preserving Parametric Inference: A Case for Robust Statistics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 969-983, April.
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    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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