Preserving privacy and frequent sharing patterns for social network data publishing
Author:
Affiliation:
1. Concordia University, Montreal, QC, Canada
2. Huazhong University of Science and Technology, Hubei University of Economics, P. R. China
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2492517.2492603
Reference17 articles.
1. Privacy-preserving data publishing
2. B. Zhou and J. Pei "Preserving privacy in social networks against neighborhood attacks " in Proceedings of the 2008 IEEE 24th International Conference on Data Engineering 2008 pp. 506--515. 10.1109/ICDE.2008.4497459 B. Zhou and J. Pei "Preserving privacy in social networks against neighborhood attacks " in Proceedings of the 2008 IEEE 24th International Conference on Data Engineering 2008 pp. 506--515. 10.1109/ICDE.2008.4497459
3. k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
4. Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph
5. L -diversity
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2. K-anonymity against neighborhood attacks in weighted social networks;Security and Communication Networks;2015-07-22
3. Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining;Lecture Notes in Social Networks;2015
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