| 研究課題/領域番号 |
21K17737
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分60060:情報ネットワーク関連
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| 研究機関 | 早稲田大学 |
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研究代表者 |
文 鄭 早稲田大学, 理工学術院, 講師(任期付) (70822261)
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| 研究期間 (年度) |
2021-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 390千円 (直接経費: 300千円、間接経費: 90千円)
2024年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2023年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2022年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2021年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
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| キーワード | Spatiotemporal info / Blockchain / TEE / IoT / GNSS Spoofing / COVID-19 / close contact tracing / blockchain / spatiotemporal info. |
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| 研究開始時の研究の概要 |
This is a close contact tracing solution based on crypto-spatiotemporal information(CSI).It uses a blockchain platform to realize the proof of CSI and uses trusted execution environment(TEE) to ensure trustworthiness and security.It can trace close contacts while protecting personal privacy.
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| 研究実績の概要 |
This year, our research focused on enhancing the trustworthiness of spatio-temporal data and refining authentication technologies. We addressed the security shortcomings of civilian Global Navigation Satellite System (GNSS) devices by conducting GNSS spoofing experiments. Our analyses led to innovative strategies that could assist devices in identifying GNSS spoofing and acquiring precise spatio-temporal data.
Simultaneously, we made advancements in spatio-temporal information authentication. Recognizing the importance of secure and accurate geolocation data, we explored new approaches for its validation. We incorporated cryptographic techniques, secure transmission protocols, and TEE solutions to augment the reliability and trustworthiness of spatio-temporal data.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Research Progress: The research is progressing smoothly. We have conducted multiple experiments, tested numerous devices, and gathered a substantial amount of data. This extensive experimental process has allowed us to obtain a comprehensive understanding of the problem at hand, and we have made significant headway towards developing effective solutions for the same. As we continue to analyze the collected data and refine our methodologies, we expect further advancements in the near future.
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| 今後の研究の推進方策 |
For future research, we plan to delve deeper into the area: leveraging artificial intelligence (AI) to detect GNSS attacks on terminal devices
AI has shown great potential in identifying patterns and anomalies that might be indicative of GNSS attacks. We aim to design and implement AI-driven models that can accurately and efficiently detect and mitigate these attacks. Our goal is to enhance the resilience of GNSS devices against spoofing and other forms of attacks, ensuring the integrity of spatio-temporal data and the overall safety of navigation systems.
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