Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms
Summary: Proposes a three-axiom utility framework for statistical privacy and proves the average Bayesian decision error is the unique information-preservation measure (priors-agnostic). On unattributed histograms, a Bayesian post-processing algorithm empirically outperforms prior approaches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bing-Rong Lin (Pennsylvania State University)
- 2. Daniel Kifer (Pennsylvania State University)
BibTeX Citation
@inproceedings{lin_sigmod13,
title = {{Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms}},
author = {Lin, Bing-Rong and Kifer, Daniel},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2463721},
url = {https://dl.acm.org/doi/10.1145/2463676.2463721},
year = {2013}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,960 | Private Release of Graph Statistics using Ladder Functions | 2015 | SIGMOD | 9.4058693e-05 |
| 8,426 | Differentially Private Hierarchical Count-of-Counts Histograms | 2018 | VLDB | 5.4265571e-05 |
| 10,313 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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