Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions
Summary: Rigorous theory of poisoning learned indexes using linear regression over CDFs: proves the known single-point attack optimal, exposes greedy multi-point attacks as suboptimal, and characterizes optimal attacks. Derives an impact upper bound that empirically nearly matches greedy loss. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Atsuki Sato (University of Tokyo)
- 2. Martin Aumüller (IT University of Copenhagen)
- 3. Yusuke Matsui (University of Tokyo)
BibTeX Citation
@inproceedings{sato_sigmod26,
title = {{Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions}},
author = {Sato, Atsuki and Aumüller, Martin and Matsui, Yusuke},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802085},
url = {https://dl.acm.org/doi/10.1145/3802085},
year = {2026}
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