Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
With the rise of blockchain technology, crypto gambling has gained popularity for its supposed anonymity and transparency in the blockchain-based Metaverse. However, this has also brought security and compliance challenges to crypto gambling, including market manipulation, fraud, and money laundering. Like traditional casinos, crypto casinos face unfair and opaque operations, often controlled by a few anonymous shills or insiders. These hidden roles, called delegatees, are harder to identify in anonymous crypto settings. This paper systematically identifies key roles and hidden delegatees in crypto casinos to enhance regulatory oversight for cybersecurity and compliance. We propose a novel node voting method to identify key nodes and a Graph Neural Network-based approach with self-supervised learning to map remaining crypto addresses to hidden roles. Experiments on real Ethereum and TRON cases demonstrate that our method outperforms existing methods in scalability, accuracy, and interpretability, achieving higher matches with identities confirmed by judicial authorities.
Recommended Citation
Wang, Jiaxin; Mao, Qian'Ang; Sun, Hongliang; Yan, Jiaqi; and Shi, Yani, "Smoke and Mirrors: Uncovering Hidden Delegatees in Crypto Casinos" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/in/metaverse/2
Smoke and Mirrors: Uncovering Hidden Delegatees in Crypto Casinos
Hilton Waikoloa Village, Hawaii
With the rise of blockchain technology, crypto gambling has gained popularity for its supposed anonymity and transparency in the blockchain-based Metaverse. However, this has also brought security and compliance challenges to crypto gambling, including market manipulation, fraud, and money laundering. Like traditional casinos, crypto casinos face unfair and opaque operations, often controlled by a few anonymous shills or insiders. These hidden roles, called delegatees, are harder to identify in anonymous crypto settings. This paper systematically identifies key roles and hidden delegatees in crypto casinos to enhance regulatory oversight for cybersecurity and compliance. We propose a novel node voting method to identify key nodes and a Graph Neural Network-based approach with self-supervised learning to map remaining crypto addresses to hidden roles. Experiments on real Ethereum and TRON cases demonstrate that our method outperforms existing methods in scalability, accuracy, and interpretability, achieving higher matches with identities confirmed by judicial authorities.
https://aisel.aisnet.org/hicss-58/in/metaverse/2