Paper 2024/1504
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Abstract
In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic encryption. In this article, we show that PEFL does not preserve privacy. In particular, we illustrate that PEFL reveals the entire gradient vector of all users in clear to one of the participating entities, thereby violating privacy. Furthermore, we clearly show that an immediate fix for this issue is still insufficient to achieve privacy by pointing out multiple flaws in the proposed system.
Note: Although our privacy issues mentioned in Section II have been published in January 2023 (Schneider et. al., IEEE TIFS'23), several subsequent papers continued to reference Liu et al. (IEEE TIFS'21) as a potential solution for private federated learning. While a few works have acknowledged the privacy concerns we raised, several of subsequent works either propagate these errors or adopt the constructions from Liu et al. (IEEE TIFS'21), thereby unintentionally inheriting the same privacy vulnerabilities. We believe this oversight is partly due to the limited visibility of our comments paper at TIFS'23 (Schneider et. al., IEEE TIFS'23). Consequently, to prevent the continued propagation of the flawed algorithms in Liu et al. (IEEE TIFS'21) into future research, we also put this article to an ePrint.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Minor revision. IEEE Transactions on Information Forensics and Security
- DOI
- 10.1109/TIFS.2023.3238544
- Keywords
- Federated LearningFLHomomorphic EncryptionPoisoning and Inference AttacksData Privacy
- Contact author(s)
-
schneider @ encrypto cs tu-darmstadt de
suresh @ encrypto cs tu-darmstadt de
yalame @ encrypto cs tu-darmstadt de - History
- 2024-09-30: approved
- 2024-09-25: received
- See all versions
- Short URL
- https://ia.cr/2024/1504
- License
-
CC BY-SA
BibTeX
@misc{cryptoeprint:2024/1504, author = {Thomas Schneider and Ajith Suresh and Hossein Yalame}, title = {Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/1504}, year = {2024}, doi = {10.1109/TIFS.2023.3238544}, url = {https://eprint.iacr.org/2024/1504} }