Results 31 to 40 of about 8,199 (263)
Fast De-anonymization of Social Networks with Structural Information
Data Science and Engineering, 2019 Ever since the social networks became the focus of a great number of researches, the privacy risks of published network data have also raised considerable concerns.
Yingxia Shao +4 more
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NeuroImage, 2021 Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition.
Christopher G. Schwarz +11 more
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Research of De-Anonymizing Method Based on Machine Learning for Bitcoin [PDF]
Jisuanji gongcheng, 2021 Bitcoin is one of the most widely used digital assets.It is a kind of decentralized anonymous cryptocurrency, and has no sovereignty or geographical restrictions.However, its anonymity makes it abused in illegal activities.To realize de-anonymization for
GUO Wensheng, YANG Xia, FENG Zhiqi, ZHANG Luchen, YANG Jinglin
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De-identifying Spanish medical texts - named entity recognition applied to radiology reports
Journal of Biomedical Semantics, 2021 Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical
Irene Pérez-Díez +4 more
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Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration
Bioengineering, 2022 Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code.
Ricardo Bruña +5 more
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An Automated Social Graph De-anonymization Technique [PDF]
Proceedings of the 13th Workshop on Privacy in the Electronic Society, 2014 We present a generic and automated approach to re-identifying nodes in anonymized social networks which enables novel anonymization techniques to be quickly evaluated. It uses machine learning (decision forests) to matching pairs of nodes in disparate anonymized sub-graphs.
Kumar Sharad, George Danezis
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An Abstract View on the De-anonymization Process
CoRR, 2019 Over the recent years, the availability of datasets containing personal, but anonymized information has been continuously increasing. Extensive research has revealed that such datasets are vulnerable to privacy breaches: being able to reveal sensitive information about individuals through deanonymization methods.
Alexandros Bampoulidis, Mihai Lupu
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A Concentration of Measure Approach to Database De-anonymization [PDF]
2019 IEEE International Symposium on Information Theory (ISIT), 2019 In this paper, matching of correlated high-dimensional databases is investigated. A stochastic database model is considered where the correlation among the database entries is governed by an arbitrary joint distribution. Concentration of measure theorems such as typicality and laws of large numbers are used to develop a database matching scheme and ...
Farhad Shirani 0001 +2 more
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Patterns, 2021 Summary: The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse.
Karthik Murugadoss +10 more
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NeuroImage: Reports, 2021 For a number of years, facial features removal techniques such as ‘defacing’, ‘skull stripping’ and ‘face masking/blurring’, were considered adequate privacy preserving tools to openly share brain images.
Damian Eke +12 more
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