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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information.
Raymond Chi-Wing Wong +3 more
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Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information.
Raymond Chi-Wing Wong +3 more
openaire +1 more source
k-Anonymization with Minimal Loss of Information
IEEE Transactions on Knowledge and Data Engineering, 2007The technique of k-anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose two information-theoretic measures for capturing the amount of ...
Aristides Gionis, Tamir Tassa
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k-Anonymization in the Presence of Publisher Preferences
IEEE Transactions on Knowledge and Data Engineering, 2011Privacy constraints are typically enforced on shared data that contain sensitive personal attributes. However, owing to its adverse effect on the utility of the data, information loss must be minimized while sanitizing the data. Existing methods for this purpose modify the data only to the extent necessary to satisfy the privacy constraints, thereby ...
Rinku Dewri +3 more
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K-anonymity on sensitive transaction items
2011 IEEE International Conference on Granular Computing, 2011K-anonymity-based techniques [9], [11], [15]–[17] have been the main anonymization techniques on relational data ad transactional data to protect privacy against re-identification attacks. Assuming the existence of both sensitive attributes and quasi-identifier (QI) attributes, a relational dataset D is k-anonymous if every record in D has at least k-1
Shyue-Liang Wang +3 more
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k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data
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From K-anonymity to Differential Privacy back to K -anonymity!
2013The research community has left no stone unturned in devising strategies for both syntactic and semantic privacy definitions. The literature on privacy protection reveals that no privacy model is capable of incorporating growing demands of data publication (e.g., the adversarial background, needs of data publisher, constraints on underlying dataset etc.
Anjum, Adeel +2 more
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Anonymity on blockchain based e-cash protocols—A survey
Computer Science Review, 2021, Nitish Andola
exaly
Are we braver in cyberspace? Social media anonymity enhances moral courage
Computers in Human Behavior, 2023Yubo Hou, Qi Wang, Xinyu Pan
exaly