John M. Abowd

Artificial Intelligence · 2002–2018

A researcher in the Science corpus: 10 papers; trusts 36 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #6,851 of 35,345 by trust here, #7,241 by citations
• Privacy & Security — 38 citations into it; #176 of 709 by trust here, #171 by citations
• Sociology and Political Science — 28 citations into it; #2,457 of 22,756 by trust here, #2,394 by citations
• Statistics and Probability — 20 citations into it
• Economics and Econometrics — 16 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Management Science and Operations Research — 10 citations into it

Whom John M. Abowd trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Cynthia Dwork
2. Kobbi Nissim
3. B. Hirsch
4. Donald B. Rubin
5. Frank McSherry
6. Jerome P. Reiter
7. Ashwin Machanavajjhala
8. Irit Dinur
9. Moritz Hardt
10. Daniel Kifer

Who trusts John M. Abowd overall

1. Josh Kinsler
2. Ragnhild Balsvik
3. Borja Balle
4. Zhiqi Bu
5. Badih Ghazi
6. Paulo Guimarães
7. Yang Yao
8. R. Buddin
9. Todd A. Gormley
10. Elsa Leromain

Papers from John M. Abowd's perspective

Ordered by how strongly the researchers John M. Abowd trusts endorse each paper. Same corpus, a different order for every researcher.

1. Revealing information while preserving privacy
2. Calibrating Noise to Sensitivity in Private Data Analysis
3. Privacy, accuracy, and consistency too: a holistic solution to contingency table release
4. On the geometry of differential privacy
5. Privacy Preserving Data Mining
6. Security-control methods for statistical databases: a comparative study
7. Practical privacy: the SuLQ framework
8. Limiting privacy breaches in privacy preserving data mining
9. Smooth sensitivity and sampling in private data analysis
10. L-diversity: privacy beyond k-anonymity

Their papers

• Privacy: Theory meets Practice on the Map (2008) — 663 citations
• The U.S. Census Bureau Adopts Differential Privacy (2018) — 462 citations
• An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices (2018) — 140 citations
• Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017) — 69 citations
• How Will Statistical Agencies Operate When All Data Are Private? (2017) — 17 citations
• Economic Analysis and Statistical Disclosure Limitation (2016) — 34 citations
• Why Statistical Agencies Need to Take Privacy-loss Budgets Seriously, and What It Means When They Do (2016)
• Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data (2013) — 146 citations
• Wages, Mobility and Firm Performance: Advantages and Insights from Using Matched Worker–Firm Data* (2006) — 126 citations
• Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data (2002) — 539 citations