Cryptology ePrint Archive: Report 2018/679

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

Jiasi Weng and Jian Weng and Jilian Zhang and Ming Li and Yue Zhang and Weiqi Luo

Abstract: 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 paper, 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 DeepChain prototype and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.

Category / Keywords: Deep learning, Privacy-preserving training, Blockchain, Incentive

Date: received 15 Jul 2018, last revised 13 Nov 2019

Contact author: wengjiasi at gmail com

Available format(s): PDF | BibTeX Citation

Note: We revised the paper by adjusting the structure of the article.

Version: 20191114:030253 (All versions of this report)

Short URL: ia.cr/2018/679


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