2022
DOI: 10.1109/tifs.2022.3208471
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Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph

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Cited by 37 publications
(7 citation statements)
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“…𝐶(𝑡) = ∑ ∑ ∫ 0 𝑡 𝑚𝑘𝑆 (𝑘,𝑙) (𝑡)𝜃(𝑡)𝑑𝑡 𝐿 𝑙=1 𝐾 𝑘=1 (11) The forwarding cumulative amount can be directly obtained from Sina Weibo. In the proposed model, the rate of change of the cumulative amount of users' forwarding over time with degree (k, l) is represented in Equation (10). Therefore, it is possible to obtain the forwarding accumulation 𝐶(𝑡) for a single message, as indicated in Equation (11).…”
Section: A Model Constructionmentioning
confidence: 99%
See 1 more Smart Citation
“…𝐶(𝑡) = ∑ ∑ ∫ 0 𝑡 𝑚𝑘𝑆 (𝑘,𝑙) (𝑡)𝜃(𝑡)𝑑𝑡 𝐿 𝑙=1 𝐾 𝑘=1 (11) The forwarding cumulative amount can be directly obtained from Sina Weibo. In the proposed model, the rate of change of the cumulative amount of users' forwarding over time with degree (k, l) is represented in Equation (10). Therefore, it is possible to obtain the forwarding accumulation 𝐶(𝑡) for a single message, as indicated in Equation (11).…”
Section: A Model Constructionmentioning
confidence: 99%
“…They underscored that network media information transmission network is self-organized, requiring four necessary conditions and certain driving forces for its realization. At the same time, scholars use self-organization charts to study the spread of disease [5][6][7][8][9][10][11][12][13][14] , being very similar to the spread of information. In summary, user selforganized network information dissemination is prevalent across various social platforms, and understanding selforganized network characteristics and user attributes is crucial for studying dissemination and guiding information governance.…”
Section: Introductionmentioning
confidence: 99%
“…In an environment where the anonymity of cryptocurrencies has led to their widespread use in financial crimes, Akcora et al [42] propose a cryptocurrency-based ransomware detection framework that can be used to automatically detect ransomware. In addition, a series of data modeling and transaction tracking methods have been proposed [43]- [50].…”
Section: A Academic Researchesmentioning
confidence: 99%
“…Their approach highlights the importance of modeling edge heterogeneity for accurate node classification in blockchain networks. Lastly, Zhou et al designed Ethident for de-anonymization across multiple Ethereum transaction datasets [10]. Their method showcased the benefits of a unified approach to de-anonymization across various types of transactions.…”
Section: Related Workmentioning
confidence: 99%