Privacy-Preserving Data Analysis for the Federal Statistical Agencies

@article{Abowd2017PrivacyPreservingDA,
  title={Privacy-Preserving Data Analysis for the Federal Statistical Agencies},
  author={John M. Abowd and Lorenzo Alvisi and Cynthia Dwork and Sampath Kannan and Ashwin Machanavajjhala and Jerome P. Reiter},
  journal={CoRR},
  year={2017},
  volume={abs/1701.00752}
}
Government statistical agencies collect enormously valuable data on the nation's population and business activities. Wide access to these data enables evidence-based policy making, supports new research that improves society, facilitates training for students in data science, and provides resources for the public to better understand and participate in their society. These data also affect the private sector. For example, the Employment Situation in the United States, published by the Bureau of… CONTINUE READING
Related Discussions
This paper has been referenced on Twitter 4 times. VIEW TWEETS

From This Paper

Topics from this paper.

Citations

Publications citing this paper.
Showing 1-4 of 4 extracted citations

Integrating Technical and Legal Concepts of Privacy

IEEE Access • 2018
View 5 Excerpts
Highly Influenced

A Data Reconstruction Method for The Big-Data Analysis

2018 9th International Conference on Awareness Science and Technology (iCAST) • 2018
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 16 references

Differentially Private Regression Diagnostics

2016 IEEE 16th International Conference on Data Mining (ICDM) • 2016

The Algorithmic Foundations of Differential Privacy

Aaron Roth
Foundations and Trends in Theoretical Computer Science • 2016

The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science

C. Dwork, Aaron Roth
Journal of American Statistical Association, • 2016

Understanding Social and Economic Data, Cornell University online course, https://www.vrdc.cornell.edu/info747x

John Abowd, Lars Vilhuber
IEEE ICDM 2016, • 2016

Similar Papers

Loading similar papers…