Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics
Summary: Releases linked employer-employee tabulations under provable privacy using Pufferfish-based, ER-EE-specific definitions. Census Bureau data experiments show utility at epsilon ≥ 1 comparable to or better than SDL for many queries, though some complex queries remain challenging. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Samuel Haney (Duke University)
- 2. Ashwin Machanavajjhala (Duke University)
- 3. John M. Abowd (United States Census Bureau)
- 4. Matthew Graham (United States Census Bureau)
- 5. Mark Kutzbach (United States Census Bureau)
- 6. Lars Vilhuber (Cornell University)
BibTeX Citation
@inproceedings{haney_sigmod17,
title = {{Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics}},
author = {Haney, Samuel and Machanavajjhala, Ashwin and Abowd, John M. and Graham, Matthew and Kutzbach, Mark and Vilhuber, Lars},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035940},
url = {https://dl.acm.org/doi/10.1145/3035918.3035940},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 596 | Private Analysis of Graph Structure | 2011 | VLDB | 0.00015926024 |
| 836 | Recursive Mechanism: Towards Node Differential Privacy and Unrestricted Joins | 2013 | SIGMOD | 0.00013715654 |
| 1,230 | No Free Lunch in Data Privacy | 2011 | SIGMOD | 0.00011572271 |
| 1,790 | Publishing Graph Degree Distribution with Node Differential Privacy | 2016 | SIGMOD | 9.7502278e-05 |
| 2,562 | Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies | 2014 | SIGMOD | 8.4127196e-05 |
| 3,375 | Bayesian Differential Privacy on Correlated Data | 2015 | SIGMOD | 7.4639817e-05 |
| 5,027 | A Rigorous and Customizable Framework for Privacy | 2012 | PODS | 6.3969504e-05 |
| 12,079 | Design of Policy-Aware Differentially Private Algorithms | 2016 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,935 | Output Perturbation with Query Relaxation | 2008 | VLDB |
| 2 | 11,203 | Personalized Truncation for Personalized Privacy | 2024 | SIGMOD |
| 3 | 4,407 | Towards an Axiomatization of Statistical Privacy and Utility | 2010 | PODS |
| 4 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 5 | 7,639 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB |
| 6 | 1,746 | The Boundary Between Privacy and Utility in Data Publishing | 2007 | VLDB |
| 7 | 8,253 | Privately Answering Queries on Skewed Data via Per-Record Differential Privacy | 2024 | VLDB |
| 8 | 4,302 | Pufferfish Privacy Mechanisms for Correlated Data | 2017 | SIGMOD |
| 9 | 5,027 | A Rigorous and Customizable Framework for Privacy | 2012 | PODS |
| 10 | 2,562 | Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies | 2014 | SIGMOD |