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The Complexities of Differential Privacy for Survey Data
[Submitted on 13 Aug 2024 (v1), last revised 31 Aug 2026 (this v · 2024-08-14 · via stat updates on arXiv.org

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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 chapter 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.

Submission history

From: James Bailie [view email]
[v1] Tue, 13 Aug 2024 16:15:42 UTC (90 KB)
[v2] Mon, 31 Aug 2026 21:16:45 UTC (139 KB)