De-Anonymizing Monero: A Maximum Weighted Matching-Based Approach

Authors

Xingyu Yang (ORCID: https://orcid.org/0000-0002-2796-776X)
Liehuang Zhu (ORCID: https://orcid.org/0000-0003-3277-3887)
Lei Xu (ORCID: https://orcid.org/0000-0001-6435-6055)

Institutions

Beijing Institute of Technology (CN)

Publication Details

Journal: IEEE Transactions on Information Forensics and Security
Published: 2025-01-01
DOI: https://doi.org/10.1109/tifs.2025.3560193
Citations: 1
Primary Topic: Privacy-Preserving Technologies in Data
Type: article
Field-Weighted Citation Impact: 2.48

Funders

National Natural Science Foundation of China
National Key Research and Development Program of China

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article

De-Anonymizing Monero: A Maximum Weighted Matching-Based Approach

Xingyu Yang, Liehuang Zhu, Lei Xu

Jan 1, 2025

1 citations

IEEE Transactions on Information Forensics and Security

Privacy-Preserving Technologies in Data

2.48

article

De-Anonymizing Monero: A Maximum Weighted Matching-Based Approach

Xingyu Yang, Liehuang Zhu, Lei Xu

January 1, 2025article en

1 citations

Abstract

No abstract available for this paper.

IEEE Transactions on Information Forensics and SecurityVol. 20

Beijing Institute of Technology (CN)

National Natural Science Foundation of China, National Key Research and Development Program of China

Openalex Percentile: Top 11%

Privacy-Preserving Technologies in Data

2.48

DOI Source OpenAlex

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De-Anonymizing Monero: A Maximum Weighted Matching-Based Approach — Xingyu Yang, Liehuang Zhu, et al. · IEEE Transactions on Information Forensics and Security (2025) | TGRS Research Map | TGRS