A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive “ASCON” with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAF-MCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost.

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Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2024-10-09
DOI
https://doi.org/10.1109/tce.2024.3473930
Citations
10
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
11.32
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article

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

Abdallah Aldosary, Muhammad Tanveer, Louai A. Maghrabi, Ahmed A. Abd El‐Latif et al.
10 citations
IEEE Transactions on Consumer Electronics
Privacy, Security, and Data Protection
11.32
article

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

Abdallah Aldosary, Muhammad Tanveer, Louai A. Maghrabi, Ahmed A. Abd El‐Latif, Musheer Ahmad, Amir Hussain, Emad A. Ahmed
article en
10 citations

Abstract

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive “ASCON” with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAF-MCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost.

IEEE Transactions on Consumer ElectronicsVol. 71(2)
Prince Sultan University (SA), Prince Sattam Bin Abdulaziz University (SA), South Valley University (EG), Edinburgh Napier University (GB), University of Business and Technology (SA), Jamia Millia Islamia (IN), University of Management and Technology (PK)
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Privacy, Security, and Data Protection
11.32
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