# A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census

arXiv: https://arxiv.org/abs/2312.11283 · HTML: https://mlsanity.com/paper/2312.11283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [Towards more accurate and useful data anonymity vulnerability measures](https://mlsanity.com/paper/2403.06595/) (2024)
- [Denoising the US Census: Succinct Block Hierarchical Regression](https://mlsanity.com/paper/2603.10099/) (2026)
- [DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics](https://mlsanity.com/paper/2504.18497/) (2025)

## Abstract (arXiv)

We show that individual, confidential microdata records from the 2010 U.S. Census of Population and Housing can be accurately reconstructed from the published tabular summaries. Ninety-seven million person records (every resident in 70% of all census blocks) are exactly reconstructed with provable certainty using only public information. We further show that a hypothetical attacker using our methods can reidentify with 95% accuracy population unique individuals who are perfectly reconstructed and not in the modal race and ethnicity category in their census block (3.4 million persons)--a result that is only possible because their confidential records were used in the published tabulations. Finally, we show that the methods used for the 2020 Census, based on a differential privacy framework, provide better protection against this type of attack, with better published data accuracy, than feasible alternatives.