BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

Shailendra Rathore, Yi Pan and Jong Hyuk Park
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Shailendra Rathore: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Yi Pan: Department of Computer Science, Georgia State University, Atlanta, GA 30302-5060, USA
Jong Hyuk Park: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea

Sustainability, 2019, vol. 11, issue 14, 1-15

Abstract: The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.

Keywords: IoT; deep learning; blockchain; security and privacy; collaborative deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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