Database Reconstruction Is Not So Easy and Is Different from Reidentification
Not on the map. Why?
Abstract (arXiv)
In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been suggested as the appropriate methodology to prevent these attacks. These claims have recently been taken very seriously by the U.S. Census Bureau and led them to adopt differential privacy for releasing U.S. Census data. This in turn has caused consternation among users of the Census data due to the lack of accuracy of the protected outputs. It has also brought legal action against the U.S. Department of Commerce. In this paper, we trace the origins of the claim that releasing information on a database automatically makes it vulnerable to being exposed by reconstruction attacks and we show that this claim is, in fact, incorrect. We also show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques. We further show that the geographic level at which exact counts are released is even more relevant to protection than the actual SDC method employed. Finally, we caution against confusing reconstruction and reidentification: using the quality of reconstruction as a metric of reidentification results in exaggerated reidentification risk figures.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2023.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
- 2023An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdatacited by 0
- 2026RAPID: Risk of Attribute Prediction-Induced Disclosure in Synthetic Microdatacited by 0
- 2026SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)cited by 0
- 2026SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)6 Jun 2026
- 2026RAPID: Risk of Attribute Prediction-Induced Disclosure in Synthetic Microdata9 Feb 2026
- 2025A Consensus Privacy Metrics Framework for Synthetic Data6 Mar 2025
- 2023An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata6 Nov 2023