Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges

Jiang, Bin; Li, Jianqiang; Yue, Guanghui; Song, Houbing

doi:10.1109/jiot.2021.3057419

Cited by 111 publications

(46 citation statements)

References 183 publications

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“…The main goal of such a system is to control the cost of losing privacy, called privacy budget (PB), so that it should not exceed the predefined global privacy budget (GPB). Notably, without adding computational burden, it tries to preserve the privacy of data by perturbing the weights, objective function, or outputs of DL models systematically [15] [40]. The noise added to the dataset will affect the privacy-utility trade-off.…”

Section: Introductionmentioning

confidence: 99%

Differential Privacy Preservation in Robust Continual Learning

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Differential Privacy Preservation in Robust Continual Learning

et al. 2022

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“…However, his experiment cannot directly show the students' direct choice and influence on tennis [2]. Xu [3,4] proposed Privacy-Preserving Data Mining (PPDM). e basic idea is to modify the data in such a way to effectively execute the data mining algorithm without compromising the security of the sensitive information contained in the data.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity

2021

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Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation system based on complex system big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment, P > 0.05 , which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences, P < 0.05 , indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results.

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“…On the other hand, IoT and IIoT must provide “differentiated privacy” for individuals and industrial data [ 67 , 68 , 69 ]. The contribution of IoT in Industry 4.0 has improved the average availability and sustainability of the enterprise by knowing market trends and decreasing unanticipated downturns [ 70 ].…”

Section: Resultsmentioning

confidence: 99%

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Majid

Habib

Javed

et al. 2022

Sensors

367

100

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The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

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Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges

Cited by 111 publications

References 183 publications

Differential Privacy Preservation in Robust Continual Learning

Differential Privacy Preservation in Robust Continual Learning

[Retracted] Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

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