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PACE: Poisoning Attacks on Learned Cardinality Estimation

Summary: PACE enables black-box poisoning of learned cardinality estimators, causing significant accuracy degradation. It uses a surrogate to approximate the model, solves a two-variable poisoning optimization, and trains a poison generator with an anomaly detector to mimic workload. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6908
Venue
SIGMOD
Year
2024
Pagerank
5.3940849e-05
Overall Rank
8,643 | 40.71%
DOI
10.1145/3639292

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod24,
        title = {{PACE: Poisoning Attacks on Learned Cardinality Estimation}},
        author = {Zhang, Jintao and Zhang, Chao and Li, Guoliang and Chai, Chengliang},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639292},
        url = {https://dl.acm.org/doi/10.1145/3639292},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 26 of 26 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,937 Elastic Machine Learning Algorithms in Amazon SageMaker 2020 SIGMOD 9.4524758e-05
2,452 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5584e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,662 openGauss: An Autonomous Database System 2021 VLDB 7.2166682e-05
3,688 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.201795e-05
3,762 HTAP Databases: What is New and What is Next 2022 SIGMOD 7.1449267e-05
3,792 Learned Index: A Comprehensive Experimental Evaluation 2023 VLDB 7.1220982e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
4,516 The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures 2022 SIGMOD 6.6489359e-05
4,612 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.6072026e-05
6,132 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 5.9660278e-05
7,901 Cloud Databases: New Techniques, Challenges, and Opportunities 2022 VLDB 5.5185913e-05
8,042 Grep: A Graph Learning Based Database Partitioning System 2023 SIGMOD 5.5015896e-05
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