Computing Inconsistency Measures Under Differential Privacy
Summary: DP-aware estimation of database inconsistency measures via a conflict-graph model to mitigate sensitivity. Proposes graph-projection techniques and a DP approximate vertex-cover variant to estimate three measures, with experiments on five real-world denial-constraint datasets across varying conflict-graph densities. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shubhankar Mohapatra (University of Waterloo)
- 2. Amir Gilad (Hebrew University)
- 3. Xi He (University of Waterloo)
- 4. Benny Kimelfeld (Technion)
BibTeX Citation
@inproceedings{mohapatra_sigmod25,
title = {{Computing Inconsistency Measures Under Differential Privacy}},
author = {Mohapatra, Shubhankar and Gilad, Amir and He, Xi and Kimelfeld, Benny},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725397},
url = {https://dl.acm.org/doi/10.1145/3725397},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,618 | Analyzing Deviations from Monotonic Trends through Database Repair | 2026 | SIGMOD | 4.9793485e-05 |
| 10,795 | Measuring Database Unfairness via Dependency Quantification Under Differential Privacy | 2026 | VLDB | 4.9793485e-05 |
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