A Probability Mapping-Based Privacy Preservation Method for Social Networks
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1274-8_19
Reference18 articles.
1. Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002). https://doi.org/10.1142/S0218488502001648
2. Machanavajjhala, A., Gehrke, J., Kifer, D., Muthuramakrishnan, V.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 1–52 (2007). https://doi.org/10.1145/1217299.1217302
3. Li, N., Li, T., Venkatasubramanian, S.: T-Closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 106–115 (2006)
4. Xiao, X., Tao, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 689–700 (2007)
5. Jiang, H., Pei, J., Yu, D., Yu, J., Gong, B., Cheng, X.: Applications of differential privacy in social network analysis: a survey. IEEE Trans. Knowl. Data Eng. 35(1), 108–127 (2021). https://doi.org/10.1109/TKDE.2021.3073062
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