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

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Title: Building a Secure Platform for Digital Governance Interoperability and Data Exchange Using Blockchain and Deep Learning-Based Frameworks
Authors: Varun Malik, Ruchi Mittal, Dinesh Mavaluru, Bayapa Reddy Narapureddy, S. B. Goyal, R. John Martin, Karthik Srinivasan, Amit Mittal
Source: IEEE Access, Vol 11, Pp 70110-70131 (2023)
Publisher Information: IEEE, 2023.
Publication Year: 2023
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms: Blockchain, data exchange, deep reinforcement learning, digital governance, interoperability, Internet of Things, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/10177172/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2023.3293529
Access URL: https://doaj.org/article/ae686e3d3874427ab02beee6cacce869
Accession Number: edsdoj.686e3d3874427ab02beee6cacce869
Database: Directory of Open Access Journals
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  Data: 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.
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