References
- Siddula, M., Cai, Z., & Miao, D. (2018, November). Privacy preserving online social networks using enhanced equicardinal clustering. In 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) (pp. 1-8). IEEE.
- Gangarde, R., Sharma, A., Pawar, A., Joshi, R., & Gonge, S. (2021). Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness. Electronics, 10(22), 2877. doi: https://doi.org/10.3390/electronics10222877
- Gangarde, R., Sharma, A., Pawar, A., Joshi, R., & Gonge, S. (2021). Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness. Electronics, 10(22), 2877. doi: https://doi.org/10.3390/electronics10222877
- Langari, R. K., Sardar, S., Mousavi, S. A. A., & Radfar, R. (2020). Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks. Expert Systems with Applications, 141, 112968. doi: https://doi.org/10.1016/j.eswa.2019.112968
- Wang, K., Zhao, W., Cui, J., Cui, Y., & Hu, J. (2019). A K-anonymous clustering algorithm based on the analytic hierarchy process. Journal of Visual Communication and Image Representation, 59, 76–83. doi: https://doi.org/10.1016/j.jvcir.2018.12.052
- Huang, H., Zhang, D., Xiao, F., Wang, K., Gu, J., & Wang, R. (2020). Privacy-preserving approach PBCN in social network with differential privacy. IEEE Transactions on Network and Service Management, 17(2), 931–945. doi: https://doi.org/10.1109/TNSM.2020.2982555
- Murakami, K., & Uno, T. (2018). Optimization algorithm for k-anonymization of datasets with low information loss. International Journal of Information Security, 17(6), 631–644. doi: https://doi.org/10.1007/s10207-017-0392-y
- Yu, F., Chen, M., Yu, B., Li, W., Ma, L., & Gao, H. (2018). Privacy preservation based on clustering perturbation algorithm for social network. Multimedia Tools and Applications, 77(9), 11241–11258. doi: https://doi.org/10.1007/s11042-017-5502-3
- Zouinina, S., Grozavu, N., Bennani, Y., Lyhyaoui, A., & Rogovschi, N. (2018, November). Efficient k-anonymization through constrained collaborative clustering. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 405-411). IEEE.
- Siddula, M., Li, Y., Cheng, X., Tian, Z., & Cai, Z. (2019). Anonymization in online social networks based on enhanced Equi-Cardinal clustering. IEEE Transactions on Computational Social Systems, 6(4), 809–820. doi: https://doi.org/10.1109/TCSS.2019.2928324
- Gadad, V., & Sowmyarani, C. N. (2019, December). A novel utility metric to measure information loss for generalization and suppression techniques in Privacy Preserving Data publishing. In 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (Vol. 4, pp. 1-5). IEEE.
- Bayardo, R. J., & Agrawal, R. (2005, April). Data privacy through optimal k-anonymization. In 21st International conference on data engineering (ICDE’05) (pp. 217-228). IEEE.
- Ghinita, G., Karras, P., Kalnis, P., & Mamoulis, N. (2009). A framework for efficient data anonymization under privacy and accuracy constraints. ACM Transactions on Database Systems (TODS), 34(2), 1–47. doi: https://doi.org/10.1145/1538909.1538911
- Mohapatra, A. K., & Prakash, N. (2010). Wired equivalent privacy reinvestigated. Journal of Discrete Mathematical Sciences and Cryptography, 13(2), 141–151. doi: https://doi.org/10.1080/09720529.2010.10698282
- Fadhil, S. A. (2021). Internet of Things security threats and key technologies. Journal of Discrete Mathematical Sciences and Cryptography, 1-7.
- Alam, I., & Kumar, S. (2021). Functionality, privacy, security and rewarding based on fog assisted cloud computing techniques in Internet of Vehicles. Journal of Discrete Mathematical Sciences and Cryptography, 24(3), 763–775. doi: https://doi.org/10.1080/09720529.2020.1794516