The Complexities of Differential Privacy for Survey Data

Joerg Drechsler and James Bailie

No 32905, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C42 C81 C83 D82 (search for similar items in EconPapers)
Date: 2024-09
New Economics Papers: this item is included in nep-ipr
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Forthcoming: The Complexities of Differential Privacy for Survey Data , Jörg Drechsler, James Bailie. in Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences , Gong, Hotz, and Schmutte. 2024

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