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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
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 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.
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From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [21]
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey
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“A similar blockchain-based model is also introduced in [169] to provide data confidentiality, computation auditability, and incentives for the participants of FL.”
BackgroundA survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
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“The incentive mechanisms may be helpful [79], [80], [185].”
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“…efforts that utilize traditional Privacy-Enhancing Technologies (e.g., zero-knowledge proofs, differential privacy, and federated learning) to tackle privacy challenges posed by LLMs. Exploring additional PETs techniques, whether they are established methods or innovative approaches, to…”
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Self-citation
Metadata
- Authors
- Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W.
- Affiliations
- Jinan University (CN)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2019
- Volume
- 2018
- Pages
- 1-1
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W2889157463
- Cited by
- 610
- References
- 67
- Citations by year
- 2026: 22 · 2025: 60 · 2024: 102 · 2023: 126 · 2022: 112 · 2021: 102 · 2020: 55 · 2019: 27
- FWCI
- 33.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1736203, 61732021, 61472165, 61373158)
- Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve (2016B010124009)
- National Key RD Program of China (Grant No. 2018YFB1402600)
- National Key RD Plan of China (2017YFB0802203, 2018YFB1003701)
- Guangdong Provincial Engineering Technology Research Center on Network Security Detection and Defence (2014B090904067)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- privacy-preserving deep learning, federated learning, blockchain, incentive mechanisms, auditability, data privacy, malicious server, participant behavior, distributed deep learning
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