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Counterfactual Explanation at Will, with Zero Privacy Leakage
Summary: CPC: client-side, data-driven counterfactual explanations that work even without model-owner cooperation, restoring user control over the right-to-explain. Key novelty: formal zero-privacy-leakage guarantee against model extraction, with complexity analysis plus fast, high-quality algorithms.
(summarized by gpt-5.4-mini on May 24 2026)
Paper ID
h8288bb7adb70d4f8
Venue
SIGMOD
Year
2024
Pagerank
4.9793485e-05
Overall Rank
11,515 | 22.58%
DOI
10.1145/3654933
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BibTeX Citation
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@inproceedings{an_sigmod24,
title = {{Counterfactual Explanation at Will, with Zero Privacy Leakage}},
author = {An, Shuai and Cao, Yang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654933},
url = {https://dl.acm.org/doi/10.1145/3654933},
year = {2024}
}
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Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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17
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2011
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0.00016096504
670
A Formal Approach to Finding Explanations for Database Queries
2014
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2015
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2012
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2022
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Causality and Explanations in Databases
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9.2586863e-05
2,222
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2016
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8.8109051e-05
2,458
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2011
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2021
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7.6912904e-05
4,283
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2016
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6.6890865e-05
4,566
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2022
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6.5310568e-05
4,729
Interactive Summarization and Exploration of Top Aggregate Query Answers
2018
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4,840
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5,178
Explainable AI: Foundations, Applications, Opportunities for Data Management Research
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2023
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5.8587542e-05
6,486
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2024
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