Enhanced k-Anonymity model based on clustering to overcome Temporal attack in Privacy Preserving Data Publishing
Author:
Affiliation:
1. R V College Of Engineering,Department Of Computer Science and Engineering,Bangalore,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9865098/9865099/09865682.pdf?arnumber=9865682
Reference24 articles.
1. A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing;kiran;International Journal of Computer Applications,2012
2. Protecting respondents identities in microdata release
3. L-diversity: privacy beyond k-anonymity
4. T-closeness: Privacy beyon d K-Anonymity and 1-diversity;li;Proc IEEE 23rd Int Conf Data Eng,2007
5. Efficient k-Anonymization Using Clustering Techniques
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1. Score, Arrange, and Cluster: A Novel Clustering-Based Technique for Privacy-Preserving Data Publishing;IEEE Access;2024
2. Flexible k-anonymity Scheme Suitable for Different Scenarios in Social Networks;IFIP Advances in Information and Communication Technology;2024
3. Information Loss characteristics in Privacy preservation approach using anonymization approach;2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM);2023-11-28
4. Hierarchical DP-K Anonymous Data Publishing Model Based on Binary Tree;2023 25th International Conference on Advanced Communication Technology (ICACT);2023-02-19
5. Analysis and Optimization of Clustering-based Privacy Preservation using Machine Learning;2023 International Conference for Advancement in Technology (ICONAT);2023-01-24
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