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
Bandara, E.; Foytik, P.; Shetty, S.; Hassanzadeh, A.
Generative-AI(with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT, Federated Learning and PBOM Enabled Data Security Architecture for Metaverse on 5G/6G Environment Proceedings Article
In: Proc. - IEEE Int. Conf. Mob. Ad-Hoc Smart Syst., MASS, pp. 118–124, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036399-9 (ISBN).
Abstract | Links | BibTeX | Tags: 5G, 6G, Adversarial machine learning, Bill of materials, Block-chain, Blockchain, Curricula, Data privacy, Distance education, Federated learning, Generative adversarial networks, Generative-AI, Hardware security, Llama2, LLM, Medium access control, Metaverse, Metaverses, Network Security, Nft, Non-fungible token, Personnel training, Problem oriented languages, Reference architecture, Steganography
@inproceedings{bandara_generative-aicustom-trained_2024,
title = {Generative-AI(with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT, Federated Learning and PBOM Enabled Data Security Architecture for Metaverse on 5G/6G Environment},
author = {E. Bandara and P. Foytik and S. Shetty and A. Hassanzadeh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210243120&doi=10.1109%2fMASS62177.2024.00026&partnerID=40&md5=70d21ac1e9c7b886da14825376919cac},
doi = {10.1109/MASS62177.2024.00026},
isbn = {979-835036399-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Mob. Ad-Hoc Smart Syst., MASS},
pages = {118–124},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core. © 2024 IEEE.},
keywords = {5G, 6G, Adversarial machine learning, Bill of materials, Block-chain, Blockchain, Curricula, Data privacy, Distance education, Federated learning, Generative adversarial networks, Generative-AI, Hardware security, Llama2, LLM, Medium access control, Metaverse, Metaverses, Network Security, Nft, Non-fungible token, Personnel training, Problem oriented languages, Reference architecture, Steganography},
pubstate = {published},
tppubtype = {inproceedings}
}
The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core. © 2024 IEEE.
2022
Wang, A.; Gao, Z.; Lee, L. H.; Braud, T.; Hui, P.
Decentralized, not Dehumanized in the Metaverse: Bringing Utility to NFTs through Multimodal Interaction Proceedings Article
In: ACM Int. Conf. Proc. Ser., pp. 662–667, Association for Computing Machinery, 2022, ISBN: 978-145039390-4 (ISBN).
Abstract | Links | BibTeX | Tags: AI-generated art, Arts computing, Behavioral Research, Computation theory, Continuum mechanics, Decentralised, Human behaviors, Interaction, Multi-modal, multimodal, Multimodal Interaction, NFTs, Non-fungible token, Text-to-image, The metaverse
@inproceedings{wang_decentralized_2022,
title = {Decentralized, not Dehumanized in the Metaverse: Bringing Utility to NFTs through Multimodal Interaction},
author = {A. Wang and Z. Gao and L. H. Lee and T. Braud and P. Hui},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142799074&doi=10.1145%2f3536221.3558176&partnerID=40&md5=f9dee1e9e60afc71c4533cbdee0b98a7},
doi = {10.1145/3536221.3558176},
isbn = {978-145039390-4 (ISBN)},
year = {2022},
date = {2022-01-01},
booktitle = {ACM Int. Conf. Proc. Ser.},
pages = {662–667},
publisher = {Association for Computing Machinery},
abstract = {User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behavior. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We present a continuum theory and propose a framework combining a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behavior data into generative NFT units, as "multimodal interactive NFT."Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content. © 2022 ACM.},
keywords = {AI-generated art, Arts computing, Behavioral Research, Computation theory, Continuum mechanics, Decentralised, Human behaviors, Interaction, Multi-modal, multimodal, Multimodal Interaction, NFTs, Non-fungible token, Text-to-image, The metaverse},
pubstate = {published},
tppubtype = {inproceedings}
}
User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behavior. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We present a continuum theory and propose a framework combining a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behavior data into generative NFT units, as "multimodal interactive NFT."Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content. © 2022 ACM.