Privacy preserving fine-grained location-based access control for mobile cloud

Baseri, Yaser; Hafid, Abdelhakim; Cherkaoui, Soumaya

doi:10.1016/j.cose.2017.10.014

Cited by 17 publications

(5 citation statements)

References 16 publications

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“…In order to meet the needs of the company's internal mobile office, Google designed and implemented a relatively stable zero trust network model BeyondCorp [36], in which the authorized access rights after identity authentication need to be obtained by granting the access agent authorization information through the "access control engine." It adopts the ZTN method for access control, but does not describe in detail the implementation of policy language, risk management, or decision-making continuity, and does not fully consider the inheritance and reuse of existing networks, just visualize its security capabilities as a product.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Survey on Zero Trust Architecture: Challenges and Future Trends

Huang

Chen

et al. 2022

Wireless Communications and Mobile Computing

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The traditional perimeter-based network protection model cannot adapt to the development of current technology. Zero trust is a new type of network security model, which is based on the concept of never trust and always verify. Whether the access subject is in the internal network or the external network, it needs to be authenticated to access resources. The zero trust model has received extensive attention in research and practice because it can meet the new network security requirements. However, the application of zero trust is still in its infancy, and enterprises, organizations, and individuals are not fully aware of the advantages and disadvantages of zero trust, which greatly hinders the application of zero trust. This paper introduces the existing zero trust architecture and analyzes the core technologies including identity authentication, access control, and trust assessment, which are mainly relied on in the zero trust architecture. The main solutions under each technology are compared and analyzed to summarize the advantages and disadvantages, as well as the current challenges and future research trends. Our goal is to provide support for the research and application of future zero trust architectures.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Survey on Zero Trust Architecture: Challenges and Future Trends

Huang

Chen

et al. 2022

Wireless Communications and Mobile Computing

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“…T. Baseri et al [16] proposed a multi-authority attribute-based access control scheme to support the coexistence of authorities. The proposed scheme used the dynamic location of mobile users as contextual information about those users, employed location range constraints as a policy in attribute-based encryption, and authorized users with dynamic locations satisfying access policies.…”

Section: Fine-grained Access Controlmentioning

confidence: 99%

A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism

Dai

Chen²

2022

CIT. J. comput. inf. technol

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People increasingly rely on their mobile devices and use them to store a lot of data. Some of the data are personal and private, whose leakage leads to users' privacy harm. Meanwhile, mobile apps and services over-collect users' data due to the coarse-grained access control approach utilized by the mobile operating system. We propose a cloud-based approach to provide fine-grained access control toward data requests. We add privacy level, as a new metadata, to data and manage the storage using different policies correspondingly. However, the proposed approach leads to performance decreases because of the extra communication cost. We also introduce a novel cache mechanism to eliminate the extra cost by storing non-private and popular data on the mobile device. As part of our cache mechanism, we design a user-preference-based ordering method along with the principle of locality to determine how popular some data are. We also design a configurable refresh policy to improve the overall performance. Finally, we evaluate our approach using a real phone in a simulated environment. The results show that our approach can keep the response time of all data requests within a reasonable range and the cache mechanism can further improve the performance.

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“…In [ 100 ], the authors proposed a framework of access control and verification schemes based on location-controlled functions named location-constrained roles-based access control (LCRBAC). The authors describe cyberspace and physical space as a static topology for the LCRBAC scheme by describing cyber objects and physical object dynamic behaviors.…”

Section: Taxonomy Of Ml-based Aa For Iotmentioning

confidence: 99%

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

Ahmed

Tahir

Habaebi

et al. 2021

Sensors

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With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security. Securing IoT networks such as the electric power grid or water supply systems has emerged as a major national and global priority. To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing. Authentication and authorization are crucial aspects of the CIA triad to protect the network from malicious parties. However, existing authorization and authentication schemes are not sufficient for handling security, due to the scale of the IoT networks and the resource-constrained nature of devices. In order to overcome challenges due to various constraints of IoT networks, there is a significant interest in using machine learning techniques to assist in the authentication and authorization process for IoT. In this paper, recent advances in authentication and authorization techniques for IoT networks are reviewed. Based on the review, we present a taxonomy of authentication and authorization schemes in IoT focusing on machine learning-based schemes. Using the presented taxonomy, a thorough analysis is provided of the authentication and authorization (AA) security threats and challenges for IoT. Furthermore, various criteria to achieve a high degree of AA resiliency in IoT implementations to enhance IoT security are evaluated. Lastly, a detailed discussion on open issues, challenges, and future research directions is presented for enabling secure communication among IoT nodes.

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Privacy preserving fine-grained location-based access control for mobile cloud

Cited by 17 publications

References 16 publications

A Survey on Zero Trust Architecture: Challenges and Future Trends

A Survey on Zero Trust Architecture: Challenges and Future Trends

A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

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