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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
- 6847
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.3679174e-05
- Overall Rank
- 9,215 | 35.96%
- DOI
-
10.1145/3639292
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
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 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 627 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018959896 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,239 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013091459 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 2,080 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5954034e-05 |
| 2,164 |
Elastic Machine Learning Algorithms in Amazon SageMaker |
2020 |
SIGMOD |
9.3953268e-05 |
| 3,241 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.32744e-05 |
| 3,269 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3026051e-05 |
| 3,455 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0760196e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,151 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4020605e-05 |
| 4,382 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2318984e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.0953507e-05 |
| 4,592 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.056004e-05 |
| 5,072 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7121108e-05 |
| 5,310 |
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures |
2022 |
SIGMOD |
5.5713508e-05 |
| 5,528 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.4571136e-05 |
| 6,860 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.9008421e-05 |
| 8,095 |
Grep: A Graph Learning Based Database Partitioning System |
2023 |
SIGMOD |
4.5837691e-05 |
| 8,150 |
Cloud Databases: New Techniques, Challenges, and Opportunities |
2022 |
VLDB |
4.5714701e-05 |
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