Building a Secure Platform for Digital Governance Interoperability and Data Exchange Using Blockchain and Deep Learning-Based Frameworks

被引:10
作者
Malik, Varun [1 ]
Mittal, Ruchi [1 ]
Mavaluru, Dinesh [2 ]
Narapureddy, Bayapa Reddy [3 ]
Goyal, S. B. [4 ]
Martin, R. John [5 ]
Srinivasan, Karthik
Mittal, Amit [6 ]
机构
[1] Chitkara Univ, Inst Engn & Technol, Rajpura 140401, India
[2] Saudi Elect Univ, Coll Comp & Informat, Riyadh 13316, Saudi Arabia
[3] King Khalid Univ, Coll Appl Med Sci, Dept Publ Hlth, Abha 62529, Saudi Arabia
[4] City Univ Malaysia, Fac Informat Technol, Petaling Jaya 46100, Malaysia
[5] Jazan Univ, Fac Comp Sci & Informat Technol, Jazan 45142, Saudi Arabia
[6] Chitkara Univ, Chitkara Business Sch, Rajpura 140401, India
关键词
Blockchain; data exchange; deep reinforcement learning; digital governance; interoperability; Internet of Things; voting system; waste management; cybersecurity; WASTE MANAGEMENT; SMART; TECHNOLOGY; PERFORMANCE; FUTURE;
D O I
10.1109/ACCESS.2023.3293529
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services. Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use. In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks. Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments. Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation. This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems. In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data. This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT. Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies. Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.
引用
收藏
页码:70110 / 70131
页数:22
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