[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Li, Zhi‐Ru; Xu, Wei; Shi, Hongyi; Zhang, Yuanyuan; Yan, Yan

doi:10.1155/2021/2398460

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…In addition to the above parameters, each user has advised the input of each recommendation R x , x = A , B , C of all users, that is, p x ( τ ), to make the final decision and action a ( τ ). Given the actions of the users regarding the selection of the recommendation, the users take part in a distributed noncooperative game for the determination of the visit time, played in each repetition of the machine learning algorithm, to regulate the optimal visit time as well as the corresponding value of their QoE [ 33 , 35 , 36 ].…”

Section: Modelingmentioning

confidence: 99%

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Cyber Risk Recommendation System for Digital Education Management Platforms

Yin¹,

Chen²

2022

Computational Intelligence and Neuroscience

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Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm’s ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.

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Section: Modelingmentioning

confidence: 99%

“…The combined QoE function of user i , i ∈ N , N = N a ∪ N b ∪ N f ∪ N g is convex in the strategy interval Τ i ′ corresponding to the interval of the relevant time ratio [ 19 , 33 , 36 , 39 ].

…”

Section: Modelingmentioning

confidence: 99%

Cyber Risk Recommendation System for Digital Education Management Platforms

Yin¹,

Chen²

2022

Computational Intelligence and Neuroscience

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“…The evaluation method specifies the technical requirements related to the privacy protection capability's test and evaluation process, including evaluation analysis, preparation, method selection, steps, and documents. Evaluation indicators specify the requirements of various indicators involved in SHPP capability testing and evaluation, such as stability, reliability, compliance, probability of privacy attribute disclosure, and block correlation [31]. Competency certification mainly handles the privacy protection level of an organization, product, service, and other related competency certification requirements, including privacy management competency certification and product privacy security certification.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

The Development of Privacy Protection Standards for Smart Home

Liu¹,

Yang

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of network technology, society has entered the Internet of Things (IoT) era. Enriching IoT devices enable smart home privacy information to be easily obtained through data mining. Smart home privacy protection (SHPP) has a farreaching impact on the development of all aspects of society. Industrial and academic circles pay great attention to the privacy protection of the smart home, and the ability to protect the privacy needs to be improved. SHPP standards are one way to evaluate privacy protection capabilities at this stage. Based on our research on privacy protection standards, this paper summarizes the development of privacy protection standards for the first time. It divides them into three phases: creation, exploration, and expansion. According to the characteristics of the privacy data rotation, the SHPP standard system is proposed, including basic commonality, critical technology, auxiliary management, test and certification, and device application. Finally, we focus on the opportunity and challenges of smart home privacy security standards brought by the current big data and prospect the trend of privacy protection technologies and standard study. They provide a connection for subsequent standard investigations.

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“…However, the data collected by these organizations can be reused repeatedly, making users difficult to delete. Besides, these sensitive data may contain unique personal identical information such as faces and voices, which inevitably bring risks when stolen by malicious attackers and used for illegal benefits [10,11].…”

Section: Introductionmentioning

confidence: 99%

Domain Transferred Image Recognition via Generative Adversarial Network

Qian

et al. 2022

Security and Communication Networks

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Recent studies have demonstrated that neural networks exhibit excellent performance in information hiding and image domain transfer. Considering the tremendous progress that deep learning has made in image recognition, we explore whether neural networks can recognize the imperceptible image in the transferred domain. Our target is to transfer natural images into images that belong to a different domain, while at the same time, the attribute of natural images can be recognized on domain transferred images directly. To address this issue, we proposed domain transferred image recognition to achieve image recognition directly on the transferred images without the original images. In our proposed system, a generator is designed for the domain transfer and a recognizer is responsible for image recognition. To be flexible for the natural image restoration in some cases, we also incorporate an additional generator in our method. In addition, a discriminator will play an indispensable role in the image domain transfer. Finally, we demonstrate that our method can successfully identify the natural images on transferred images without access to original images.

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[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Cited by 13 publications

References 24 publications

Cyber Risk Recommendation System for Digital Education Management Platforms

Cyber Risk Recommendation System for Digital Education Management Platforms

The Development of Privacy Protection Standards for Smart Home

Domain Transferred Image Recognition via Generative Adversarial Network

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