Learning in Your “Pocket”: Secure Collaborative Deep Learning With Membership Privacy

Organizations tend to collaboratively train the deep learning model over their combined datasets for a common benefit (e.g., better-trained model or learning a complicated model). However, due to the consideration about privacy leakage, organizations cannot share their data directly, especially related to sensitive domains. In this article, a privacy-preserving collaborative deep learning mechanism, namely Sigma, is designed to allow participating organizations to train a collective model without exposing their local training data to the others. Specifically, a single-server-aided private collaborative architecture is introduced to achieve the private collaborative learning, which protects organizations’ data even if
$n-1$
out of
$n$
participants colluded. We also design a practical protocol to perform the secure model training, which can resist the typical inference attack through the sharing information. After that, we propose a fair model releasing mechanism for participants and introduce differential privacy to prevent model stealing and membership inference attack. Furthermore, we prove that Sigma can ensure participants’ privacy preservation and analyze the communication overhead in theory. To evaluate the effectiveness and efficiency of Sigma, we conduct an experiment over two real-world datasets and the simulation results demonstrate that Sigma can efficiently achieve the collaborative model training and effectively resist the membership inference attack.