Lightweight Fine-Grained Search Over Encrypted Data in Fog Computing

Miao, Yinbin; Ma, Jianfeng; Liu, Ximeng; Weng, Jian; Li, Hongwei; Li, Hui

doi:10.1109/tsc.2018.2823309

Cited by 144 publications

(68 citation statements)

References 35 publications

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“…Fog computing is not a completely new paradigm, but it is an extension of cloud computing for IoT applications . The relation among IoT devices, fog nodes, and cloud platform is shown in Figure …”

Section: Preliminary Conceptsmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

144

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Section: Preliminary Conceptsmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

144

View full text Add to dashboard Cite

show abstract

“…Very recently, although the CP-ABKS [17] that achieves fine-grained access control and search functionality is proposed, its computational and storage overhead is still much more larger. In addition, the reliability of the converted ciphertext returned from the cloud server is not verified.…”

Section: Related Workmentioning

confidence: 99%

“…With respect to function, storage cost, and computation overhead, this part presents comparisons among other existing attribute based keyword search schemes and our proposed scheme. Table 1 shows the function comparison with other schemes [13][14][15][16][17]. From the Table 1, we can learn that all schemes provide fine-grained keyword search.…”

Section: Performance Analysismentioning

confidence: 99%

“…From the Table 1, we can learn that all schemes provide fine-grained keyword search. Data sharing is achieved in our scheme and [14,16,17]. Verifiable decryption among [14,16,17] and ours is only dealt with in our scheme.…”

Section: Performance Analysismentioning

confidence: 99%

“…Data sharing is achieved in our scheme and [14,16,17]. Verifiable decryption among [14,16,17] and ours is only dealt with in our scheme. In summary, our scheme is only one that can achieve all functionalities including fine-grained keyword search and data sharing and verifiable decryption.…”

Section: Performance Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Lightweight Fine-Grained Search Scheme over Encrypted Data in Cloud-Assisted Wireless Body Area Networks

Cao

Wang

Qin

et al. 2019

Wireless Communications and Mobile Computing

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The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients' demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.

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The blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for federated learning model in IoMT

Zhou,

Wang,

Liu

et al. 2024

Concurrency and Computation

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Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization.

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Lightweight Fine-Grained Search Over Encrypted Data in Fog Computing

Cited by 144 publications

References 35 publications

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

A Lightweight Fine-Grained Search Scheme over Encrypted Data in Cloud-Assisted Wireless Body Area Networks

The blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for federated learning model in IoMT

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