Results 11 to 20 of about 8,199 (263)
Neural-based inexact graph de-anonymization
High-Confidence ComputingGraph de-anonymization is a technique used to reveal connections between entities in anonymized graphs, which is crucial in detecting malicious activities, network analysis, social network analysis, and more.
Guangxi Lu +5 more
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Survey of Bitcoin de-anonymization technology
Tongxin xuebaoThe Bitcoin system, based on blockchain technology, features decentralization, borderlessness, and anonymity, drawing widespread attention from academia and industry.
CHENG Jie +7 more
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De-anonymiation method for networks based on DeepLink
网络与信息安全学报, 2020 Existing de-anonymization technologies are mainly based on the network structure. To learn and express network structure is the key step of de-anonymization.
WANG Pei, JIA Yan, LI Aiping, JIANG Qianyue
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Finding the Sweet Spot for Data Anonymization: A Mechanism Design Perspective
IEEE Access, 2022 Data sharing between different organizations is an essential process in today’s connected world. However, recently there were many concerns about data sharing as sharing sensitive information can jeopardize users’ privacy.
Abdelrahman Eldosouky +3 more
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De-anonymization Attacks on Neuroimaging Datasets [PDF]
Proceedings of the 2021 International Conference on Management of Data, 2021 Advances in imaging technologies, combined with inexpensive storage, have led to an explosion in the volume of publicly available neuroimaging datasets. Effective analyses of these images hold the potential for uncovering mechanisms that govern functioning of the human brain, and understanding various neurological diseases and disorders.
Vikram Ravindra, Ananth Grama
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APPLICATION OF PRIVACY-PRESERVING DATA PUBLISHING IN TERTIARY INSTITUTIONS OF KEBBI STATE USING GENERALIZATION AND SUPPRESSION [PDF]
Journal of Engineering Science (Chişinău), 2022 The research was conducted in the field of publishing data to preserve confidentiality. Several educational datasets have been used to address privacy and utility.
SHEHU, Anas +2 more
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Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
Applied Sciences, 2023 Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens. However, the massive data generated in these cities also poses significant privacy risks, particularly in de-anonymization and re-identification. This survey
Silvio Sampaio +5 more
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De-anonymizing Social Networks [PDF]
2009 30th IEEE Symposium on Security and Privacy, 2009 Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
Arvind Narayanan, Vitaly Shmatikov
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De-anonymization Attack on Geolocated Data [PDF]
2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013 With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular ...
Sébastien Gambs +2 more
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Computational Linguistics, 2022 We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently ...
Ildikó Pilán +5 more
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