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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
- 6846
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.3721075e-05
- Overall Rank
- 9,213 | 35.91%
- 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 |
0.00059038975 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 629 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018942366 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011049779 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010836769 |
| 2,083 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5834572e-05 |
| 2,163 |
Elastic Machine Learning Algorithms in Amazon SageMaker |
2020 |
SIGMOD |
9.3949234e-05 |
| 3,248 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.3258782e-05 |
| 3,266 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3074684e-05 |
| 3,449 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0824319e-05 |
| 3,473 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.062864e-05 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 4,152 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4060406e-05 |
| 4,284 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2914924e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.1011198e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 5,074 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7175726e-05 |
| 5,428 |
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures |
2022 |
SIGMOD |
5.5091613e-05 |
| 5,645 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.3923454e-05 |
| 6,879 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.8971368e-05 |
| 7,543 |
Cloud Databases: New Techniques, Challenges, and Opportunities |
2022 |
VLDB |
4.715241e-05 |
| 8,103 |
Grep: A Graph Learning Based Database Partitioning System |
2023 |
SIGMOD |
4.5852201e-05 |
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