Demo Data from EDBT 2026 / reviewers in the wild / expert
Difan Xie
dblp:405/7089
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security
smart contract analysis |
0.9 | 1 | 2025 | Why Is My Transaction Risky? Understanding Smart Contract Semantics and Interactions in the NFT Ecosystem · ASE 2025 |
Blockchain and cryptocurrency security
smart contract security |
0.9 | 1 | 2025 | Why Is My Transaction Risky? Understanding Smart Contract Semantics and Interactions in the NFT Ecosystem · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
transaction analysis · 0.9empirical study · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Why Is My Transaction Risky? Understanding Smart Contract Semantics and Interactions in the NFT EcosystemabstractThe NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions. Xuanming Liu, Zhiyuan Wan, Zuobin Wang, David Lo 0001, Difan Xie, Xiaohu Yang 0001 |
ASE | 6 |