TY - GEN
T1 - Dataflow Management Platform for Smart Communities using an Edge Computing Environment
AU - Shimahara, Shogo
AU - Nishi, Hiroaki
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR19K1, and the commissioned research by National Institute of Information and Communications Technology (NICT, Grant Number 22004), JAPAN.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/13
Y1 - 2021/10/13
N2 - As various data services are provided to realize Society 5.0, the usage of personal data is estimated to increase along with the explosive increase in data traffic. It is important that the protection of privacy keeps pace with increases in the exchange of data containing personal information. Starting from the enforcement of the General Data Protection Regulation (GDPR), stricter privacy protection regulations are expanding to more countries. These restrictions require that personal data should be hidden or anonymized before they are propagated over the network. The secondary usage of data is assumed in smart communities, and systems that can protect privacy are required for secure network infrastructures. In this study, we propose an edge-based computing platform that manages the privacy of users on the network of a smart community. For the platform, we prepared three models: The Basic, Preceding Packet, and Piggyback models. These are considered OpenFlow models, and the network efficiency for each was evaluated.
AB - As various data services are provided to realize Society 5.0, the usage of personal data is estimated to increase along with the explosive increase in data traffic. It is important that the protection of privacy keeps pace with increases in the exchange of data containing personal information. Starting from the enforcement of the General Data Protection Regulation (GDPR), stricter privacy protection regulations are expanding to more countries. These restrictions require that personal data should be hidden or anonymized before they are propagated over the network. The secondary usage of data is assumed in smart communities, and systems that can protect privacy are required for secure network infrastructures. In this study, we propose an edge-based computing platform that manages the privacy of users on the network of a smart community. For the platform, we prepared three models: The Basic, Preceding Packet, and Piggyback models. These are considered OpenFlow models, and the network efficiency for each was evaluated.
KW - Edge computing
KW - OpenFlow
KW - information bank
KW - network transparency
UR - http://www.scopus.com/inward/record.url?scp=85119514695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119514695&partnerID=8YFLogxK
U2 - 10.1109/IECON48115.2021.9589430
DO - 10.1109/IECON48115.2021.9589430
M3 - Conference contribution
AN - SCOPUS:85119514695
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Y2 - 13 October 2021 through 16 October 2021
ER -