Towards trusted node selection using blockchain for crowdsourced abnormal data detection

Xia, He; Yang, Haochen; Wang, Guanghui; Yu, Junyang

doi:10.1016/j.future.2022.03.026

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…Finally, three sets of known data were selected for simulation experiments to verify the reliability of the algorithm. The experimental results showed that the TWNS algorithm reduced crowdsourcing costs by 64.6% [11]. Lili M A et al proposed a way to enhance the detection capability of fiber optic communication networks to improve AD.…”

Section: Related Workmentioning

confidence: 99%

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A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors

Lv¹,

Li²,

Chen³

et al. 2023

Processes

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The basic work of power data research is anomaly detection. It is necessary to find a method suitable for processing current power system data. Research proposes an algorithm of fast density peak clustering with Local Outlier Factor (LOF). The algorithm has poor performance in processing datasets with irregular shapes and significant local density changes, and has the disadvantage of strong dependence on truncation distance. This study provides the decision rules for outliers incorporating the idea of LOF. The improved algorithm can fully consider the characteristics of power data and reduce the dependence on truncation distance. In anomaly detection based on the simulation of real power data, the classification accuracy of the improved CFSFDP algorithm is 4.87% higher than that of the traditional algorithm, and the accuracy rate is 97.41%. The missed and false detection rates of the LOF-CFSFDP algorithm are decreased by 2.23% and 2.64%, respectively, compared to the traditional algorithm, and it is ultimately able to reach rates of 1.26% and 1.33%. These results indicate that the algorithm proposed in this study can better describe the characteristics of power data, making the features of outliers and cluster center points more obvious.

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Section: Related Workmentioning

confidence: 99%

“…In Formula (11), R denotes the correlation coefficient matrix, and its eigenvalues are calculated as shown in Formula (12).…”

Section: Power Load Cluster Analysis With Improved Lofmentioning

confidence: 99%

A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors

Lv¹,

Li²,

Chen³

et al. 2023

Processes

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“…Blockchain-Based Privacy Preservation. Blockchain-assisted data aggregation has been studied to improve the performance of crowdsensing [34]. e security and integrity are improved by utilizing blockchains for data aggregation in crowdsensing.…”

Section: 2mentioning

confidence: 99%

Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing

Wang

Yang

et al. 2022

Security and Communication Networks

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Privacy-preserving data aggregation is an important technology for mobile crowdsensing. Blockchain-assisted data aggregation enables the traceability of sensing data to improve the trustworthiness of data aggregation results. However, directly using blockchains for data aggregation may introduce the risk of privacy leakage because all nodes, including malicious nodes, can access the data on blockchains. In this paper, we propose a grouping-based reliable privacy-preserving data aggregation (RPPDA) method using private blockchains for mobile crowdsensing. First, the sensing nodes are divided into multiple groups, and each group maintains a private blockchain to store the data aggregation records, which avoids the leakage of the aggregated results and ensures the traceability of the sensory data. Then, a zero-sum noise-adding mechanism is utilized to not only preserve the private information during aggregation and ensure the correctness of the aggregated results but also improve the efficiency of privacy preservation. Furthermore, we theoretically prove the correctness, privacy, efficiency, and reliability of the proposed RPPDA algorithm. Real-world and simulated experiments demonstrate the effectiveness and advantages of the proposed RPPDA algorithm in terms of correctness, efficiency, and privacy.

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“…Internet of vehicles (IoV) is a new paradigm in the future Internet of things, where vehicles sense external information through on-board equipment on the road [1][2][3][4]. Vehicles can obtain huge amounts of data (e.g., video data), and the circulation of data among vehicles is one of the most effective means to improve the efciency of data utilization and fully explore the value of vehicular data [5][6][7][8]. However, due to concerns about personal privacy data leakage, vehicle owners are unwilling to upload data to the data center, which hinders the fow of data and afects the development of the IoV industry [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

He,

Hu,

Wang

et al. 2023

Security and Communication Networks

Self Cite

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Federated learning is an enabling technology for the services in Internet of vehicles because it can effectively alleviate privacy issues in data circulation and diversified intelligent applications. However, existing federated learning methods still confront the problem of low computational efficiency when applied to the scenario of high-dynamic vehicles. In order to address this problem, we present a multistep federated learning architecture including task release, vehicle model training, identity authentication, quality assessment, and weighted aggregation processes, where a novel agile aggregation model is established. Based on the agile aggregation model, an efficient federated learning (EFL) algorithm is proposed, which is specifically applied to the scenario of high-dynamic vehicles during the training model. We theoretically analyze its advantages over the existing traditional federated learning algorithms in terms of efficiency, training accuracy, and security. Simulations are conducted using the MNIST dataset to evaluate the performance of the proposed EFL algorithm. The results show that EFL improves the efficiency of the federated training process with effective accuracy and privacy preservation.

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Towards trusted node selection using blockchain for crowdsourced abnormal data detection

Cited by 14 publications

References 20 publications

A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors

A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors

Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

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