| Title |
TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning |
| ID_Doc |
42217 |
| Authors |
Kumar, P; Gupta, GP; Tripathi, R |
| Title |
TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.sysarc.2020.101954 |
| Abstract |
With the advancement in sensor technology and the proliferation of low-cost electronic circuits, Internet of Things (IoT) is emerging as a promising technology for realization of smart cities. However, challenges such as security, privacy, trust, scalability, verifiability, and centralization prevent faster adaptations of IoT-driven smart cities. Thus, in this paper, a Trustworthy Privacy-Preserving Secured Framework (TP2SF) for smart cities is presented. This framework includes three modules namely: a trustworthiness module, a two-level privacy module, and an intrusion detection module. In trustworthiness module, address-based blockchain reputation system is designed. In the two-level privacy module, a blockchain based enhanced Proof of Work (ePoW) technique is simultaneously applied with Principal Component analysis (PCA) to transform data into a new reduced shape for preventing inference and poisoning attacks. In the intrusion detection module, an optimized gradient tree boosting system (XGBoost) is deployed. Finally, due to inherited strengths and weaknesses of Fog?Cloud architecture, we present a blockchain-IPFS integrated Fog?Cloud infrastructure namely, CloudBlock and FogBlock to deploy proposed TP2SF framework in smart city. The TP2SF framework is evaluated using two realistic IoT-based datasets, namely ToN-IoT and BoT-IoT. The findings indicate the superiority of TP2SF framework over other state-of-the-art techniques in both nonblockchain and blockchain systems. |
| Author Keywords |
Machine learning; Blockchain; Smart city; Anomaly detection; Privacy-preservation; Trust management; Internet of Things |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000639862100003 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
|