DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive

Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this article, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a prototype of DeepChain and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.

Automated summary

Deep learning achieves higher accuracy than traditional machine learning algorithms in various tasks, while privacy-preserving deep learning has recently gained significant attention. Federated learning, a popular mechanism, may neglect security issues such as incorrect behavior from participants or malicious actions from the server. In this article, a distributed, secure, and fair deep learning framework named DeepChain is introduced to address these problems, with a focus on incentive mechanisms and data privacy.