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Towards an Axiomatization of Statistical Privacy and Utility

Summary: Axiomatic framework for statistical privacy and utility under randomized algorithms, characterizing their interaction and guiding mechanism design. Identifies a class of differential-privacy relaxations and argues DP outputs are best interpreted as graphs rather than query answers or synthetic data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1514
Venue
PODS
Year
2010
Pagerank
6.719141e-05
Overall Rank
4,407 | 69.77%
DOI
10.1145/1807085.1807106

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kifer_pods10,
        address = {New York, NY, USA},
        series = {{PODS} '10},
        title = {{Towards an Axiomatization of Statistical Privacy and Utility}},
        url = {https://dl.acm.org/doi/10.1145/1807085.1807106},
        doi = {10.1145/1807085.1807106},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Kifer, Daniel and Lin, Bing-Rong},
        year = {2010}
}

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