AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Truong, V. T.; Le, H. D.; Le, L. B.
Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse Journal Article
In: IEEE Access, vol. 12, pp. 41815–41828, 2024, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality
@article{truong_trust-free_2024,
title = {Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse},
author = {V. T. Truong and H. D. Le and L. B. Le},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188472793&doi=10.1109%2fACCESS.2024.3376509&partnerID=40&md5=301939c1faef0c5a7b56d9feadce27ee},
doi = {10.1109/ACCESS.2024.3376509},
issn = {21693536 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {41815–41828},
abstract = {The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.},
keywords = {AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.
2023
Basdekis, Ioannis; Kloukinas, Christos; Agostinho, Carlos; Vezakis, Ioannis; Pimenta, Andreia; Gallo, Luigi; Spanoudakis, Georgios
Pseudonymisation in the Context of GDPR-compliant Medical Research Proceedings Article
In: 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 1–6, IEEE, Vilanova i la Geltru, Spain, 2023, ISBN: 978-1-66547-598-3.
Abstract | Links | BibTeX | Tags: GDPR, Healthcare, Security
@inproceedings{basdekisPseudonymisationContextGDPRcompliant2023,
title = {Pseudonymisation in the Context of GDPR-compliant Medical Research},
author = { Ioannis Basdekis and Christos Kloukinas and Carlos Agostinho and Ioannis Vezakis and Andreia Pimenta and Luigi Gallo and Georgios Spanoudakis},
doi = {10.1109/DRCN57075.2023.10108370},
isbn = {978-1-66547-598-3},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
booktitle = {2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)},
pages = {1--6},
publisher = {IEEE},
address = {Vilanova i la Geltru, Spain},
abstract = {Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.},
keywords = {GDPR, Healthcare, Security},
pubstate = {published},
tppubtype = {inproceedings}
}
Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.
Basdekis, Ioannis; Kloukinas, Christos; Agostinho, Carlos; Vezakis, Ioannis; Pimenta, Andreia; Gallo, Luigi; Spanoudakis, Georgios
Pseudonymisation in the context of GDPR-compliant medical research Proceedings Article
In: 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 1–6, IEEE, Vilanova i la Geltru, Spain, 2023, ISBN: 978-1-66547-598-3.
Abstract | Links | BibTeX | Tags: GDPR, Healthcare, Security
@inproceedings{basdekis_pseudonymisation_2023,
title = {Pseudonymisation in the context of GDPR-compliant medical research},
author = {Ioannis Basdekis and Christos Kloukinas and Carlos Agostinho and Ioannis Vezakis and Andreia Pimenta and Luigi Gallo and Georgios Spanoudakis},
url = {https://ieeexplore.ieee.org/document/10108370/},
doi = {10.1109/DRCN57075.2023.10108370},
isbn = {978-1-66547-598-3},
year = {2023},
date = {2023-04-01},
urldate = {2023-05-09},
booktitle = {2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)},
pages = {1–6},
publisher = {IEEE},
address = {Vilanova i la Geltru, Spain},
abstract = {Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.},
keywords = {GDPR, Healthcare, Security},
pubstate = {published},
tppubtype = {inproceedings}
}
Pseudonymisation is a data protection technique often used to protect the privacy of individuals when their personal data are being used for research purposes. Not only is it a key ingredient of the General Data Protection Regulation (GDPR) that requires organisations to ensure that the personal data they process is handled in a secure manner, but it is particularly important in assisting medical research given that often relies on sensitive personal data, since it reduces the risk that medical data could be misused or mishandled. For managing their medical data, it is important to ensure that such data are protected against unauthorised access, and can be reutilised in an anonymous fashion, while still authorised personnel is able to identify the study participant that some data belong to (e.g., for personalised interventions, technical alerts, technical support). In addition, the re-identification of a study participant is a pre-requisite for exercising their rights under the GDPR, since it assists organisations in meeting GDPR requirements (such as the right to access, rectify and portability of data). We argue that the application of pseudonymisation is particularly effective when considered during the early stages (Privacy by Design) of digital services implementation, as well as when defining the complementary to these organizational procedures. Aim of this paper is to present the way in which the pseudonymisation mechanism of the SMART BEAR H2020 project supports the triptych of research activities conducted within the context of an observational medical study, legal obligations arising from the regulatory framework for the protection of personal data, and reutilisation of data for research purposes. Evidence-based security and privacy assessments will be conducted on two different H2020 projects to evaluate such privacy practice.