Utility-based anonymization using local recoding

By Jian Xu, Wei Wang, Jian Pei, Xiaoyuan Wang, Baile Shi, Ada Wai-Chee Fu. Knowledge Discovery and Data Mining, 2006

485 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/118059ec0740ddb3333f85f9ec79834289e99ec8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Benjamin C. M. Fung
3. Ke Wang
4. Rui Chen
5. Jian Pei
6. Wo-Shun Luk
7. Charu C. Aggarwal
8. Bin Zhou

In Sociology and Political Science

1. Philip S. Yu
2. Benjamin C. M. Fung
3. Rui Chen
4. Ke Wang
5. Jian Pei
6. Ada Wai-Chee Fu
7. Raymond Chi-Wing Wong
8. Bin Zhou

Citations in the corpus

• Privacy-preserving anonymization of set-valued data (2008, methodology): “[22] propose agglomerative and divisive recursive clustering algorithms, which attempt to minimize the NCP metric (to be described in Section 3.3).”
• Preserving Privacy in Social Networks Against Neighborhood Attacks (2008, background): “The information loss due to generalization of vertex labels can be measured by the normalized certainty penalty [21].”
• Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “Top-Down Disclosure.”
• A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “Greedy approaches have been widely used in privacy preservation in relational data [34; 15; 10], and have been shown effective.”
• Anonymizing Classification Data for Privacy Preservation (2007, methodology): “ANY Edu M [1-99) 20Y6N 26 ANY Edu F [1-99) 0Y14N 14”
• Privacy Preservation in the Publication of Trajectories (2008, background): “In the same direction, [20] propose more efficient agglomerative and divisive recursive clustering algorithms for this problem.”
• Minimality Attack in Privacy Preserving Data Publishing (2007, background): “The idea is similar to the normalized certainty penalty [26].”
• Fast Data Anonymization with Low Information Loss (2007, methodology): “In the following experiments, we compare our 1-D optimal k-anonymity algorithm against the existing state-ofthe-art techniques: The multi-dimensional (Mondrian) kanonymity [10], and the TopDown clustering-based technique [21] (see Section 2 for details).”