Randomness Concerns when Deploying Differential Privacy

@article{Garfinkel2020RandomnessCW,
  title={Randomness Concerns when Deploying Differential Privacy},
  author={S. Garfinkel and P. Leclerc},
  journal={Proceedings of the 19th Workshop on Privacy in the Electronic Society},
  year={2020}
}
  • S. Garfinkel, P. Leclerc
  • Published 2020
  • Computer Science
  • Proceedings of the 19th Workshop on Privacy in the Electronic Society
The U.S. Census Bureau is using differential privacy (DP) to protect confidential respondent data collected for the 2020 Decennial Census of Population & Housing. The Census Bureau's DP system is implemented in the Disclosure Avoidance System (DAS) and requires a source of random numbers. We estimate that the 2020 Census will require roughly 90TB of random bytes to protect the person and household tables. Although there are critical differences between cryptography and DP, they have similar… Expand
2 Citations
The Discrete Gaussian for Differential Privacy
  • 21
  • PDF

References

SHOWING 1-5 OF 5 REFERENCES
Parallel random numbers: As easy as 1, 2, 3
  • 192
  • Highly Influential
Non-determinism and overcount on modern hardware performance counter implementations
  • 84
  • Highly Influential
  • PDF
Intel (R) Digital Random Number Generator (DRNG) Software
  • Implementation Guide. (Oct
  • 2018
Census TopDown Algorithm: Differentially Private Data, Incremental Schemas, and Consistency with Public Knowledge
  • (Oct. 2019)
  • 2019
Small Fast Chaotic (SFC) 64
  • http://pracrand.sourceforge.net/ Last accessed July
  • 2016