2024/08/13 by Jörg Drechsler, Drechsler, Jörg, James Bailie +1 · 1 citation
Computer Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Methodology (stat.ME) #Privacy-Preserving Technologies in Data #Survey Methodology and Nonresponse
paper · pdf · doi:10.48550/arxiv.2408.07006
openalex publication_date 2024/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.