Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

Shen, Yizhou; Shen, Shigen; Li, Qi; Zhou, Haiping; Wu, Zongda; Qu, Youyang

doi:10.1016/j.dcan.2022.05.004

Cited by 52 publications

(14 citation statements)

References 55 publications

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“…The adoption of blockchain-based federated learning in the healthcare industry has seen significant progress, especially in the Internet of Things (IoT) and smart healthcare applications [9,34,36]. Internet of Things devices collect real-time patient data, and federated learning enables them to build robust healthcare models while keeping patient data private within their respective systems.…”

Section: Figurementioning

confidence: 99%

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A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

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Section: Figurementioning

confidence: 99%

“…Safeguarding these data and ensuring its management and security present considerable challenges for healthcare organizations. With the increasing awareness of data breaches and privacy concerns, users are becoming more cautious about how their personal information is handled and protected [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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“…In order to enhance the security of IoT communication, an elliptic curve cryptographic asymmetric information encryption algorithm is proposed 35 to protect the information data in the IoT. An evolutionary privacy protection learning strategy based on edge computing for IoT data sharing scheme is proposed 36 to effectively solve the privacy protection during the data sharing of IoT.…”

Section: Related Workmentioning

confidence: 99%

“…It is significantly superior to the prior art in speed Practical application is too ideal Liu and Zhang 35 IoT networks Improve information storage speed in the IoT Security and storage need to be further improved Shen et al 36 IoT…”

Section: Realize Optimal Relay Selection and Cooperative Controlmentioning

confidence: 99%

Evolutionary modeling and robustness analysis of multi-domain cooperative communication network under the environment of Internet of things

Wang

Zhang

Wang

et al. 2022

International Journal of Distributed Sensor Networks

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In the future, the Internet of things will reduce the cell radius and increase the number of low-power nodes to support thousands of times of traffic growth under 5G. As a virtual multiple input multiple output technology, cooperative communication technology can solve these problems effectively. According to the evolution characteristics of cooperative communication networks, a multi-domain cooperative communication network evolution model with preferential attachment and random attachment is constructed in this article. And then, the network properties and robustness are analyzed using the mean-field method and different attacks. Aiming at the resource constraints and resource allocation problems of communication nodes, a relay selection strategy based on the combination of maximum degree and minimum clustering coefficient is proposed. The simulation results show that the relay node selection strategy based on the combination of maximum degree and minimum clustering coefficient has significant advantages in selection steps and selection time, which greatly enhanced the performance of relay selection in multi-domain cooperative communication networks. Through real-time monitoring and updating of the performance and security indicators of the multi-domain cooperative communication networks, it provides a strong guarantee for the node deployment and security management of the Internet of things cooperative communication system.

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“…With the rapid development of the Internet of Things and 5G/6G networks, smart mobile devices have accelerated their upgrading [ 1 , 2 ]. The popularity of smart mobile devices has also promoted the application of location-based services (LBS) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A strategy to balance location privacy and positioning accuracy

He,

Liu,

2024

PLoS ONE

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In privacy protection methods based on location services, constructing anonymous areas using location information shared by collaborative users is the main method. However, this collaborative process not only increases the risk of mobile users’ location privacy being leaked, but also reduces positioning accuracy. In response to this problem, we propose a balancing strategy, which transforms the problem of protecting mobile users’ location privacy and improving positioning accuracy into a balance issue between location privacy and positioning accuracy. The cooperation of mobile users with different collaborating users is then modeled as an objective optimization problem, and location privacy and positioning accuracy are evaluated separately to make different selection strategies. Finally, an optimization function is constructed to select the optimal selection strategies. Experimental results show that our proposed strategy can effectively achieve the balance between location privacy and positioning accuracy.

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Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

Cited by 52 publications

References 55 publications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Evolutionary modeling and robustness analysis of multi-domain cooperative communication network under the environment of Internet of things

A strategy to balance location privacy and positioning accuracy

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