Results 21 to 30 of about 24,103,638 (294)
Data Anonymization: K-anonymity Sensitivity Analysis [PDF]
2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 2020 These days the digitization process is everywhere, spreading also across central governments and local authorities. It is hoped that, using open government data for scientific research purposes, the public good and social justice might be enhanced. Taking into account the European General Data Protection Regulation recently adopted, the big challenge ...
Santos, Wilson +3 more
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A decision-support framework for data anonymization with application to machine learning processes
Information Sciences, 2022 The application of machine learning techniques to large and distributed data archives might result in the disclosure of sensitive information about the data subjects.
Loredana Caruccio +4 more
semanticscholar +1 more source
Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey
IEEE Access, 2021 Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to
Abdul Majeed, Sungchang Lee
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Data Anonymization Process Challenges and Context Missions
International Journal of Database Management Systems, 2023 Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing ...
H. Tahir, P. Brézillon
semanticscholar +1 more source
Scalable Distributed Data Anonymization for Large Datasets
IEEE Transactions on Big Data, 2023 $k$k-Anonymity and $\ell$ℓ-diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information.
S. De Capitani di Vimercati +7 more
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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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Spectral Anonymization of Data [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2010 The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data, because an attacker can still use auxiliary information to infer sensitive individual information.
Thomas A, Lasko, Staal A, Vinterbo
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Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark
Electronics, 2021 Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts.
S. Bazai +2 more
semanticscholar +1 more source
Data anonymization patent landscape
Croatian Operational Research Review, 2017 The omnipresent, unstoppable increase in digital data has led to a greater understanding of the importance of data privacy. Different approaches are used to implement data privacy.
Mirjana Pejić Bach +2 more
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Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients
Scientific Data, 2022 Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks.
Carolin E. M. Koll +26 more
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