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Robustness of Updatable Learning-based Index Advisors against Poisoning Attack

Summary: Introduces PIPA, an opaque-box stress-test framework to evaluate the robustness of updatable learning-based Index Advisors against poisoning attacks without using private data. Probing, injecting, and IABART-based query generation reveal systemic non-robustness: subtle extraneous workloads can demote top indexes and trap IAs in local optima even after fine-tuning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6881
Venue
SIGMOD
Year
2024
Pagerank
5.2629522e-05
Overall Rank
9,490 | 34.90%
DOI
10.1145/3639265

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zheng_sigmod24,
        title = {{Robustness of Updatable Learning-based Index Advisors against Poisoning Attack}},
        author = {Zheng, Yihang and Lin, Chen and Lyu, Xian and Zhou, Xuanhe and Li, Guoliang and Wang, Tianqing},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639265},
        url = {https://dl.acm.org/doi/10.1145/3639265},
        year = {2024}
}

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