Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks

Li, Yun; Ma, Hui; Wang, Lei; Mao, Shiwen; Wang, Guoyin

doi:10.1109/tmc.2020.3033563

Cited by 151 publications

(30 citation statements)

References 40 publications

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“…In addition, we will continue to investigate the possibility of integrating our privacy-aware PERR solution with other classical privacy-preservation techniques, such as blockchain [31][32][33] , differential privacy [34,35] , anonymization [36] , and program code analyses [37,38] . Moreover, computation offloading is often necessary, especially in a big data environment [39][40][41][42][43][44][45] . Following this, it is expected that more time-efficient and energysaving versions of PERR are to remain increasingly relevant in future academic research.…”

Section: Discussionmentioning

confidence: 99%

Privacy-aware examination results ranking for the balance between teachers and mothers

Yuan

Wang

Zhao

et al. 2022

Tsinghua Sci. Technol.

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As the main parent and guardian, mothers are often concerned with the study performance of their children.More specifically, most mothers are eager to know the concrete examination scores of their children. However, with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking (PERR) method to attempt at balancing teachers' privacy disclosure concerns and the mothers' concerns over their children's examination performance.By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.

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Section: Discussionmentioning

confidence: 99%

Privacy-aware examination results ranking for the balance between teachers and mothers

Yuan

Wang

Zhao

et al. 2022

Tsinghua Sci. Technol.

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“…In the mobile edge computing environment, data collaboration among different mobile devices or edge terminals is inevitable [51][52][53][54][55][56][57][58][59]. erefore, how to secure user privacy (including privacy measurement) while guaranteeing other conflicting performances in service evaluation is an open research issue that calls for intensive study.…”

Section: Discussionmentioning

confidence: 99%

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

et al. 2021

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With the increasing growth of web services shared in various mobile edge platforms, it becomes necessary to evaluate all the candidates based on their quality of services to reduce the users’ service selection cost. However, the service quality data released by service providers cannot be simply deemed as trusted due to various subjective or objective reasons, which further produce a series of serious trust-aware service evaluation problems, including service quality data sparsity and lack of feedback incentive. In view of this, we summarize the challenging issues existing in the current research field of trusted mobile edge service evaluation. Afterward, we review the current research status of the trusted service evaluation in the mobile edge environment and discuss one of the typical application scenarios based on trusted service evaluation, that is, recommender systems, as well as their diverse categories. We believe this research could be helpful in assisting a mobile edge platform to build a trusted reputation system for various smart applications hosted in the mobile edge platform.

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“…In a federated learning-based healthcare system, AI only needs to train local models to upload parameters, while a central server updates the global picture, thus reducing the sensitive information leakage risk [ 12 , 13 ]. Being immutable and traceable [ 14 ], blockchain is an effective tool to help decentralize federated learning. Timely updates of local model parameters are uploaded by clients to the blockchain's distributed ledgers, and these model updates are audited.…”

Section: Introductionmentioning

confidence: 99%

A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain

Wang

Cai

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19’s medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model’s validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.

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Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks

Cited by 151 publications

References 40 publications

Privacy-aware examination results ranking for the balance between teachers and mothers

Privacy-aware examination results ranking for the balance between teachers and mothers

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain

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