SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. (arXiv:2205.09986v1 [cs.CR])

Secure multiparty computation (MPC) has been proposed to allow multiple
mutually distrustful data owners to jointly train machine learning (ML) models
on their combined data. However, the datasets used for training ML models might
be under the control of an adversary mounting a data poisoning attack, and MPC
prevents inspecting training sets to detect poisoning. We show that multiple
MPC frameworks for private ML training are susceptible to backdoor and targeted
poisoning attacks. To mitigate this, we propose SafeNet, a …

attacks data data poisoning machine machine learning poisoning