Designing a GDPR compliant blockchain-based IoV distributed information tracking system

Campanile, Lelio; Iacono, Mauro; Marulli, Fiammetta; Mastroianni, Michele

doi:10.1016/j.ipm.2021.102511

Cited by 66 publications

(41 citation statements)

References 27 publications

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“…With the development of information technology, information processing system became a common form of human-computer interaction, widely used in all walks of life, and an important tool for various fields to obtain and store information, so the current research on information processing system is also increasing [1]. Tan et al proposed a reservoir computing (RC) scheme for parallel processing of Santa Fe time series prediction task and signal classification task, performed a numerical research on its performance, and found that it has a very small error [2].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

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With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

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“…Most research exclusively focuses on redactability to address the right to reasure or considers specific domains or use cases, e.g. traffic management [47], e-government [33], or finance [42]. In total, previous literature on blockchain and GDPR reveals only three general design principles: Rieger et al [33] recommend avoiding personal data storage on blockchain, using private and permissioned pseudonymization approaches to process personal data, and implementing off-chain identifier mappings for the coordination of cross-organizational workflows.…”

Section: Development Of the Frameworkmentioning

confidence: 99%

Yes, I Do: Marrying Blockchain Applications with GDPR

Schellinger¹,

Völter²,

Urbach³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Due to blockchains' intrinsic transparency and immutability, blockchain-based applications are challenged by privacy regulations, such as the EU General Data Protection Regulation. Hence, scaling blockchain use cases to production often fails to owe to a lack of compliance with legal constraints. As current research mainly focuses on specific use cases, we aim to offer comprehensive guidance regarding the development of blockchain solutions that comply with privacy regulations. Following the action design research method, we contribute a generic framework and design principles to the research domain. In this context, we also emphasize the need for distinguishing between applications based on blockchains' data integrity and computational integrity guarantees.

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“…On this count, it improves the payment and dispute resolution of the transportation system [ 8 ]. A framework using blockchain technologies to support privacy-oriented, certified and shared transportation information management was proposed [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…Technological advances in Artificial Intelligence (AI) (e.g., including both machine learning and deep learning) and blockchain have proven capable to improve safety and efficiency in the smart public transport network. There have been increasing proposals of utilizing blockchain technologies to support different aspects of transport facilities and communication [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. However, there is a lack of research on utilizing blockchain technology on the public transport maintenance system to safeguard from integrity attacks that can intercept and make unauthorized changes against sensitive transport maintenance data.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

Liu

Sabrina

Jang-Jaccard

et al. 2021

Sensors

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A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.

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Designing a GDPR compliant blockchain-based IoV distributed information tracking system

Cited by 66 publications

References 27 publications

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

Yes, I Do: Marrying Blockchain Applications with GDPR

Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

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