| 1. |
- Huang, Haiping, et al.
(författare)
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An Efficient Signature Scheme Based on Mobile Edge Computing in the NDN-IoT Environment
- 2021
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Ingår i: IEEE Transactions on Computational Social Systems. - : IEEE. - 2329-924X. ; 8:5, s. 1108-1120
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Tidskriftsartikel (refereegranskat)abstract
- Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.
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| 2. |
- Lederman, Judith S., et al.
(författare)
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Completing the progression establishing an international baseline of primary, middle and secondary students’ views of scientific inquiry
- 2023
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Ingår i: International Journal of Science Education. - : Routledge. - 0950-0693 .- 1464-5289.
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of scientific inquiry (SI) is considered essential to the development of an individual's Scientific Literacy (SL) and therefore, SI is included in many international science education reform documents. Two previous large scale international studies assessed the SI understandings of students entering middle school and secondary students at the end of their formal K-12 science education. The purpose of this international project was to use the VASI-E to collect data on what primary level students have learned about SI in their first few years of school. This study adds to previous research to bridge the landscape of SI understandings now with representation from primary, middle and high school samples. A total of 4,238 students from 35 countries/regions spanning six continents participated in the study. The results show that globally, primary students are not adequately informed about SI for their age group. However, when compared with the students in the previous international studies (grades seven and 12), the primary students' understandings were surprisingly closer to the levels of understanding of SI of the secondary school students than those in the seventh grade study.
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| 3. |
- Liu, Yongshuang, et al.
(författare)
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Classification and recognition of encrypted EEG data based on neural network
- 2020
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Ingår i: Journal of Information Security and Applications. - : Elsevier. - 2214-2134 .- 2214-2126. ; 54
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions. (C) 2020 Elsevier Ltd. All rights reserved.
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