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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
6957
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,170 | 23.37%
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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Year
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17
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605
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2011
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663
A Formal Approach to Finding Explanations for Database Queries
2014
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2015
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2012
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VLDB
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1,951
Interpretable Data-Based Explanations for Fairness Debugging
2022
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9.4252389e-05
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2016
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8.9713779e-05
2,432
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2011
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8.5870067e-05
3,000
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7.8677069e-05
4,194
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2016
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6.8426144e-05
4,631
Interactive Summarization and Exploration of Top Aggregate Query Answers
2018
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6.595034e-05
4,747
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2015
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6.5252099e-05
5,025
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
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6.3974766e-05
5,054
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2023
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6,360
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HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries
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7,964
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5.1845938e-05
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2024
SIGMOD
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