TY - JOUR
T1 - BlockDeepNet
T2 - A blockchain-based secure deep learning for IoT network
AU - Rathore, Shailendra
AU - Pan, Yi
AU - Park, Jong Hyuk
N1 - Publisher Copyright:
© 2019 by the authors.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - Blockchain
KW - Collaborative deep learning
KW - Deep learning
KW - IoT
KW - Security and privacy
UR - http://www.scopus.com/inward/record.url?scp=85073013862&partnerID=8YFLogxK
U2 - 10.3390/su11143974
DO - 10.3390/su11143974
M3 - Article
AN - SCOPUS:85073013862
SN - 2071-1050
VL - 11
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 14
M1 - 3974
ER -
