# A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks

arXiv: https://arxiv.org/abs/2009.08739 · HTML: https://mlsanity.com/paper/2009.08739/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation](https://mlsanity.com/paper/2202.02628/) (2022)
- [BagFlip: A Certified Defense against Data Poisoning](https://mlsanity.com/paper/2205.13634/) (2022)
- [Run-Off Election: Improved Provable Defense against Data Poisoning Attacks](https://mlsanity.com/paper/2302.02300/) (2023)
- [On Optimal Learning Under Targeted Data Poisoning](https://mlsanity.com/paper/2210.02713/) (2022)
- [Learning and Certification under Instance-targeted Poisoning](https://mlsanity.com/paper/2105.08709/) (2021)
- [Lethal Dose Conjecture on Data Poisoning](https://mlsanity.com/paper/2208.03309/) (2022)
- [Temporal Robustness against Data Poisoning](https://mlsanity.com/paper/2302.03684/) (2023)
- [Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks](https://mlsanity.com/paper/2308.07553/) (2023)
- [PECAN: A Deterministic Certified Defense Against Backdoor Attacks](https://mlsanity.com/paper/2301.11824/) (2023)
- [UTrace: Poisoning Forensics for Private Collaborative Learning](https://mlsanity.com/paper/2409.15126/) (2024)
- [Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness](https://mlsanity.com/paper/2506.03075/) (2025)
- [On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks](https://mlsanity.com/paper/2306.16415/) (2023)

## Abstract (arXiv)

Neural network classifiers are vulnerable to data poisoning attacks, as attackers can degrade or even manipulate their predictions thorough poisoning only a few training samples. However, the robustness of heuristic defenses is hard to measure. Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set. This paper proposes a framework of random selection based certified defenses against data poisoning attacks. Specifically, we prove that the random selection schemes that satisfy certain conditions are robust against data poisoning attacks. We also derive the analytical form of the certified radius for the qualified random selection schemes. The certified radius of bagging derived by our framework is tighter than the previous work. Our framework allows users to improve robustness by leveraging prior knowledge about the training set and the poisoning model. Given higher level of prior knowledge, we can achieve higher certified accuracy both theoretically and practically. According to the experiments on three benchmark datasets: MNIST 1/7, MNIST, and CIFAR-10, our method outperforms the state-of-the-art.