Signature for Objects: Formalizing How to Authenticate Physical Data and More-Reference-Cited by-同舟云学术

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Signature for Objects: Formalizing How to Authenticate Physical Data and More

Published:2023-12-01 Issue: Volume: Page:182-199
ISSN:0302-9743
Container-title:Financial Cryptography and Data Security
language:
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Author:

Hayashi Ryuya,Asano Taiki,Hayata Junichiro,Matsuda Takahiro,Yamada Shota,Katsumata Shuichi,Sakai Yusuke,Teruya Tadanori,Schuldt Jacob C. N.,Attrapadung Nuttapong,Hanaoka Goichiro,Matsuura Kanta,Matsumoto Tsutomu

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47754-6_11

Reference33 articles.

1. Havocscope. https://havocscope.com/. Accessed 22 Aug 2022

2. Intellectual Property Rights Seizure Statistics — Fiscal Year 2019. Homeland Security (2019). https://www.iprcenter.gov/file-repository/trade-fy2017-ipr-seizures.pdf/view. Accessed 22 Aug 2022

3. Lecture Notes in Computer Science;B Barak,2014

4. Lecture Notes in Computer Science;B Barak,2001

5. Baryannis, G., Dani, S., Antoniou, G.: Predicting supply chain risks using machine learning: the trade-off between performance and interpretability. Futur. Gener. Comput. Syst. 101, 993–1004 (2019). https://doi.org/10.1016/j.future.2019.07.059

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