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The 2020 US Decennial Census is more private than you (might) think.
Buxin Su1, Weijie J Su2, Chendi Wang3,4
1Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104.
Summary
The 2020 US Census offers stronger privacy than stated, with noise reduction possible. This research shows improved accuracy for census data applications by optimizing differential privacy.
Area of Science:
- Demographic studies
- Statistical analysis
- Computer science
Background:
- The US Decennial Census is crucial for policy decisions, including funding and redistricting.
- Differential privacy was used in 2020 to protect respondent confidentiality by adding noise to data.
- The Census Bureau questioned if stronger privacy was achieved or if privacy budgets were fully utilized.
Purpose of the Study:
- To determine if the 2020 US Census provided stronger privacy protections than officially published.
- To assess if the privacy budgets for the 2020 Census were fully utilized.
Main Methods:
- Utilized ε-differential privacy to precisely track privacy losses across eight geographical levels.
- Analyzed the composition of private queries across different geographic granularities.
- Quantified the noise variances introduced by the Census Bureau's disclosure avoidance system.
Main Results:
- The 2020 US Census offers significantly stronger privacy protections than officially reported at all geographical levels.
- Noise variances could be reduced by 15.08% to 24.82% while maintaining comparable privacy.
- Reducing noise injection improves the accuracy of privatized census statistics.
Conclusions:
- The Census Bureau introduced excessive noise, indicating privacy budgets were not fully utilized.
- Optimizing noise levels enhances data utility for downstream applications, such as analyzing socioeconomic relationships.
- This research provides a method for more accurate and privacy-preserving census data.
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