… can also be used for unsupervised, privacy-preserving learning, too. Multi-layer neural
networks are the most common form of deep learning architectures. Figure 1 shows a typical …

… framework for privacy preserving deep learning and discuss … privacy preserving constructs
such as Federated Learning, … while still exposing a familiar deep learning API to the end-user. …

… privacy-preserving deep learning system in which many learning participants perform neural
network-based deep … a system for privacy-preserving deep learning, allowing local datasets …

… In this paper, we provide a review of the existing privacy-preserving deep learning … -of-the-art
privacy-preserving deep learning techniques on the basis of privacy-preserving tasks at the …

… Consequently, Privacy-Preserving Deep Learning (PPDL) arises as a … privacypreserving
techniques, starting from classical privacy-preserving techniques to well-known deep learning …

… Shokri and Shmatikov [28] presented a system for privacypreserving deep learning that
allowed local datasets of several participants staying home while the learned model for the …

… Here we present PriMIA, a free, open-source framework for end-to-end privacy-preserving
decentralized deep learning on medical images. Our framework incorporates differentially …

… We show how to achieve privacy-preserving deep learning for training encrypted datasets
under different public keys. In our two schemes, the data owners encrypt the date before …

… the first privacy-preserving federated deep learning approach … an efficient and privacy-preserving
federated deep learning scheme … scheme in federated deep learning with lightweight ho…

… In Section 4, we present our DeepChain, a framework for auditable and privacypreserving
deep learning, and analyze security properties of DeepChain in Section 5. We give …