A general framework for privacy preserving data publishing

Author:

Sarowar Sattar A.H.M.,Li Jiuyong,Ding Xiaofeng,Liu Jixue,Vincent Millist

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference48 articles.

1. k-Anonymity: a model for protecting privacy;Sweeney;International Journal on Uncertainty, Fuzziness and Knowledge-based Systems,2002

2. l-Diversity: privacy beyond k-anonymity;Machanavajjhala;ACM Transaction on Knowledge Discovery from Data,2007

3. N. Li, T. Li, S. Venkatasubramanian, t-Closeness: privacy beyond k-anonymity and l-diversity, in: IEEE International Conference on Data Engineering, 2007, pp. 106–115.

4. R.C.W. Wong, J. Li, A.W.C. Fu, K. Wang, (α,k)-Anonymity: an enhanced k-anonymity model for privacy-preserving data publishing, in: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2006, pp. 754–759.

5. J. Li, Y. Tao, X. Xiao, Preservation of proximity privacy in publishing numerical sensitive data, in: ACM SIGMOD International Conference on Management of Data, 2008, pp. 473–485.

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3. Power data publishing method based on differential privacy considering spatiotemporal correlation;2021 IEEE Sustainable Power and Energy Conference (iSPEC);2021-12-23

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