AWAP: Adaptive weighted attribute propagation enhanced community detection model for bitcoin de-anonymization

Xie, Xuhui; Jiming, Wang; Junyi, Ye; Fang, Yaozheng; Lu, Ye; Li, Tao; Wang, Guiling

doi:10.1016/j.asoc.2021.107507

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…For instance, in the case, where nodes represent patients, the latter can include attributes like age, gender, education, which leads to what is referred as a nodeattributed social network [69]. In general, a complex network is subdivided into two dimensions where the first one corresponds to the network structure and the second one corresponds to the node attributes [70,71]. Therefore, accounting for both network structure and node attributes, when available, is necessarily to yield a global and useful community [72].…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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Section: Community Detection Algorithmsmentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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“…Gambs et al [204] Poor performance on highly skewed (or non i.i.d) training data Chiasserini et al [205] De-anonymization rate drops when # of users in each cluster increase Chiasserini et al [206] Less applicability to other types (i.e., weighted, attributed, etc.) of graphs Chiasserini et al [207] Yields poor performance when total variations in graph structures are high Fu et al [208] Poor convergence when nodes degree or variations in attributes' values is high Fu et al [209] Optimal mapping conditions cannot be met in non-overlapping community cases Francia et al [210] De-anonymization rate drops when anonymization is made through DP model Orekondy et al [211] The performance can be severely impacted if no external data is available Chen et al [212] Yields poor performance in the presence of outliers/misaligned feature space Murakami et al [213] Use different variants (≃ 20) of user's locations to perform de-anonymization Li et al [214] De-anonymization rate drops significantly when no identical graphs are available Zhen et al [215] Extensive comparisons are required to infer SA when most users are similar Wang et al [216] Heavily relies on exogenous records to perform de-anonymization of data Zhang et al [217] Waste of computing time when same users are not available in both graphs Chen et al [218] The algorithmic complexity is high, and dramatically increases with graph size Ma et al [219] Many operations are performed to infer SA, and the solution is not generic Nilizadeh et al [220] Poor performance in the case of overlapping communities, or sparse graph cases Takbiri et al [221] Lack of numerical tests and uses multiple assumptions to perform SA's inference Shirani et al [222] The # of queries can significantly increase when the number of users is very large Aliakbari et al [223] Poor results in terms of matching and computing time when seeds are erroneous Xueshuo et al [224] Significant efforts are needed to convert complex data into structured data Li et al [225] Poor performance when most profile attributes are not visible externally Wang et al [226] Matching rate drops significantly in the presence of noises and mismatches Tu et al [227] Prone to less matching when co-relation among aggregated data is low Yang et al…”

Section: Sota Study Key Limitation (S)mentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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“…Previous studies treated public ledgers as "physical" artefacts: for example, research dedicated to blockchains' throughput, fees, and transaction volumes (Azevedo Sousa et al, 2021;Spain, Foley, and Gramoli, 2020;Sovbetov, 2018), Bitcoin miners (Nuzzi, 2021), and the performance of Uniswap's Automated Market Maker (AMM) protocol (Babel et al, 2021). Additional examples include: studies about the feasibility of de-anonymisation (Chan and Olmsted, 2017;Xueshuo et al, 2021), transaction frontrunning (Daian et al, 2020;Torres, Camino, and State, 2021), and blockchain performance or improvement opportunities (Saraph and Herlihy, 2020;Liang, L. Li, and Zeng, 2018).…”

Section: Data Collectionmentioning

confidence: 99%