MP-BADNet+: Secure and effective backdoor attack detection and mitigation protocols among multi-participants in private DNNs

Abstract

Deep neural networks (DNNs) significantly facilitate the performance and efficiency of the Internet of Things (IoT). However, DNNs are vulnerable to backdoor attacks where the adversary can inject malicious data during the DNN model training. Such attacks are always activated when the input is stamped with a pre-specified trigger, resulting in a pre-setting prediction of the DNN model. It is necessary to detect the backdoors whether the DNN model has been injected before implementation. Since the data come from the various data holders during the model training, it is also essential to preserve the privacy of both input data and model. In this paper, we propose a framework MP-BADNet${}^{+}$ including backdoor attack detection and mitigation protocols among multi-participants in private deep neural networks. Based on the secure multi-party computation technique, MP-BADNet${}^{+}$ not only preserves the privacy of the training data and model parameters but also enables backdoor attacks detection and mitigation in privacy-preserving DNNs. Furthermore, we give the security analysis and formal security proof following the real world-ideal world simulation paradigm. Last but not least, experimental results demonstrate that our approach is effective in detecting and mitigating backdoor attacks in privacy-preserving DNNs.