Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids

Zhao, Shuai; Li, Fenghua; Li, Hongwei; Lu, Rongxing; Ren, Siqi; Bao, Haiyong; Lin, Jianhong; Han, Sunyoung

doi:10.1109/tifs.2020.3014487

Cited by 135 publications

(59 citation statements)

References 47 publications

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“…Artificial intelligence industry refers to an industry that provides intelligent products and technical services to the society based on artificial intelligence technology. It has derived various new intelligent applications through enabling manufacturing, agriculture, medical, and other industries, such as subtopic detection [1], intelligent agriculture [2], and smart grid [3]. These applications have become a new engine to promote high-quality economic and social development.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Lin

Zhao-yang²,

Liu

et al. 2022

Wireless Communications and Mobile Computing

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In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Lin

Zhao-yang²,

Liu

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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show abstract

“…It is known from [18] that the discovery of PPI network function modules is a process in which clustering technology is used to extract similar functions from PPI network data. We all know that because the discovery of functional modules can help people predict unknown functions and understand various biological processes, they can provide an important theoretical basis for studying the mechanism of diseases and finding new drug targets [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Improved Dynamic PPI Network Construction and Application of Data Mining in Computer Artificial Intelligence Systems

Tang

2022

Scientific Programming

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The protein interaction network is a network composed of the interactions between all proteins in living organisms. The use of computational methods to prove the functional modules in the PPI network is an important research topic in bioinformatics. With the development of technologies for detecting protein interactions, the available data on interactions has become more and more extensive, and the PPI networks created have become more and more extensive. At the same time, with the deepening of research, people have found that the real PPI network is constantly changing with time and conditions and this change is closely related to the creation and development of life activities. Therefore, it is also very important and necessary to understand and study the functional modules of the PPI dynamic network. At the same time, it focuses on analyzing the basic attributes of artificial intelligence and computer technology and analyzing the goals that can be achieved by using artificial intelligence in computer technology. The effects and directions that can be further explored can be extracted from a large amount of unrealistic, incomplete, fuzzy, and random actual data. These data are hidden from people who do not know them before , but this is a potentially useful process for obtaining information and knowledge. Applying data mining technology to diagnose computer network problems can improve reliability, stability, flexibility, troubleshooting speed, and proper network management, automatically diagnose, predict, and maintain network failures, and ensure that the network has high-quality services and reliability. Drive AI applications in computer science.

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“…In other words, mining a collection of redundant items is a type of data analysis that has an essential role in mining events, patterns, and sets of repetitive items in the data. Zhao has investigated a feasible and intelligent web analytics technique for fog-based smart grids that preserves anonymity [28]. Han et al have studied a geographical crowdsourced distance calculation that keeps location privacy [29].…”

Section: Previous Research Studiesmentioning

confidence: 99%

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Salehpour

Asghari

Javadi

et al. 2022

Wireless Communications and Mobile Computing

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Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.

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Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids

Cited by 135 publications

References 47 publications

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Improved Dynamic PPI Network Construction and Application of Data Mining in Computer Artificial Intelligence Systems

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

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