Privacy-preserving fog computing paradigm

Abubaker, Nabil; Dervishi, Leonard; Ayday, Erman

doi:10.1109/cns.2017.8228709

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…132 • Usage pattern may also refer to the frequency of data being sent to the Fog nodes. 89 • The adversary can infer sensitive information (eg, Smart grid) such as at what times the user is unavailable at home and switch on and off certain appliances. 5 The traditional cryptographic techniques cannot be effective in Fog environment due to its unique characteristics.…”

Section: Challenge Category Challenge Descriptionmentioning

confidence: 99%

“…87 • Extra communication and delay due to users' mobility. 89 • Authentication overhead, redundant authentication and poor scalability. 58 • Decoy technique needs extra storage, and cannot differentiate between false positive and false negative.…”

Section: Iot Devices Privacymentioning

confidence: 99%

“…Therefore, the low latency of Fog computing will be violated. 89 • Difficult identity authentication realization due to decentralized nature of Fog computing. 62 • Periodic pseudonyms may lead to heavy computation cost in resource constraint IoT domain.…”

Section: Challenge Available Solutions Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Alzoubi

Osmanaj

Jaradat

et al. 2020

Security and Privacy

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Fog nodes are implemented near to end‐users Internet of Things (IoT) devices, which mitigate the impact of low latency, location awareness, and geographic distribution unsupported features of many IoT applications. Moreover, Fog computing decreases the data offload into the Cloud, which decreases the response time. Despite these benefits, Fog computing faces many challenges in meeting security and privacy requirements. These challenges occur due to the limitations of Fog computing resources. In fact, Fog computing may add new security and privacy issues. Although many papers have discussed the Fog security and privacy issues recently, most of these papers have discussed these issues at a very high level. This paper provides a comprehensive understanding of Fog privacy and security issue. In this survey, we review the literature on Fog computing to draw the state‐of‐the‐art of the security and privacy issues raised by Fog computing. The findings of this survey reveal that studying Fog computing is still in its infant stage. Many questions are yet to be answered to address the privacy and security challenges of Fog computing.

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Section: Challenge Category Challenge Descriptionmentioning

confidence: 99%

Section: Iot Devices Privacymentioning

confidence: 99%

See 1 more Smart Citation

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Alzoubi

Osmanaj

Jaradat

et al. 2020

Security and Privacy

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show abstract

“…Abubaker et al [15] discusses about two different approaches upon the closeness of fog nodes to the users with the interconnected application while aggregating the nearer nodes for ensuring the security by using the Trusted Third Party Node Scheme (TTPS). The advantage of this TTPS scheme is reduced latency and overhead costs.…”

Section: Privacy-preserving Fog and Edge Computing System Modelmentioning

confidence: 99%

A Four-Layer Robust Storage in Cloud using Privacy Preserving Technique with Reliable Computational Intelligence in Fog-Edge

2020

KSII TIIS

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The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2 32 blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

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“…For example industrial internet and health monitoring system needs high real time, reliability and high efficiency in millisecond. Therefore, cloud computing under the internet of things cannot fulfil requirement high mobility, location awareness and low latency requirements services [5].…”

Section: Introductionmentioning

confidence: 99%

Man in the Middle Attack Prevention for Edge-Fog, Mutual Authentication Scheme

2019

ijrte

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Fog computing is considered emerging technology nowadays. Due to proximity to the end user, fog computing provides a reliable transmission with low latency. In this paper, we have proposed an improved mutual authentication security scheme based on advanced encryption standard AES and hashed message authentication code HMAC in fog computing. Our scheme provides mutual authentication between edge devices and fog server in edge fog cloud computing environment. Further, the scheme has the resistance to man in the middle attack in the fog computing environment. Detailed security analyses are summarized.

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Privacy-preserving fog computing paradigm

Cited by 17 publications

References 17 publications

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

A Four-Layer Robust Storage in Cloud using Privacy Preserving Technique with Reliable Computational Intelligence in Fog-Edge

Man in the Middle Attack Prevention for Edge-Fog, Mutual Authentication Scheme

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