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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)

Paper ID
4716
Venue
SIGMOD
Year
2013
Pagerank
5.7980998e-05
Overall Rank
6,701 | 54.03%
DOI
10.1145/2463676.2463721

Incoming Non-self Citations Over Time

Authors

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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