Citation: | Yuanyuan WANG, Xing ZHANG, Zhiguang CHU, et al., “An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs,” Chinese Journal of Electronics, vol. 34, no. 1, pp. 1–8, 2025 doi: 10.23919/cje.2023.00.276 |
[1] |
L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557–570, 2002. doi: 10.1142/S0218488502001648
|
[2] |
A. Machanavajjhala, D. Kifer, J. Gehrke, et al., “L-diversity: Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, article no. 3, 2007.
|
[3] |
N. H. Li, T. C. Li, and S. Venkatasubramanian, “T-closeness: Privacy beyond k-anonymity and l-diversity,” in Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey, pp. 106–115, 2007.
|
[4] |
F. Ahmed, A. X. Liu, and R. Jin, “Social graph publishing with privacy guarantees,” in Proceedings of the 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan, pp. 447–456, 2016.
|
[5] |
C. Borgs, J. Chayes, A. Smith, et al., “Revealing network structure, confidentially: Improved rates for node-private graphon estimation,” in Proceedings of the 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), Paris, France, pp. 533–543, 2018.
|
[6] |
F. Ahmed, A. X. Liu, and R. Jin, “Publishing social network graph eigenspectrum with privacy guarantees,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 2, pp. 892–906, 2020. doi: 10.1109/TNSE.2019.2901716
|
[7] |
C. K. Wei, S. L. Ji, C. C. Liu, et al., “AsgLDP: Collecting and generating decentralized attributed graphs with local differential privacy,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3239–3254, 2020. doi: 10.1109/TIFS.2020.2985524
|
[8] |
E. Chicha, B. A. Bouna, M. Nassar, et al., “A user-centric mechanism for sequentially releasing graph datasets under blowfish privacy,” ACM Transactions on Internet Technology, vol. 21, no. 1, article no. 20, 2021. doi: 10.1145/3431501
|
[9] |
Z. J. Zhang, “LDPCD: A novel method for locally differentially private community detection,” Computational Intelligence and Neuroscience, vol. 2022, article no. 4080047, 2022. doi: 10.1155/2022/4080047
|
[10] |
K. Stokes and V. Torra, “Reidentification and k-anonymity: A model for disclosure risk in graphs,” Soft Computing, vol. 16, no. 10, pp. 1657–1670, 2012. doi: 10.1007/s00500-012-0850-4
|
[11] |
R. Assam, M. Hassani, M. Brysch, et al., “(k, d)-core anonymity: Structural anonymization of massive networks,” in Proceedings of the 26th International Conference on Scientific and Statistical Database Management, Aalborg, Denmark, article no. 17, 2014.
|
[12] |
R. Trujillo-Rasua and I. G. Yero, “k-metric antidimension: A privacy measure for social graphs,” Information Sciences, vol. 328, pp. 403–417, 2016. doi: 10.1016/j.ins.2015.08.048
|
[13] |
H. Rong, T. H. Ma, M. L. Tang, et al., “A novel subgraph K+- isomorphism method in social network based on graph similarity detection,” Soft Computing, vol. 22, no. 8, pp. 2583–2601, 2018. doi: 10.1007/s00500-017-2513-y
|
[14] |
S. Mauw, Y. Ramírez-Cruz, and R. Trujillo-Rasua, “Conditional adjacency anonymity in social graphs under active attacks,” Knowledge and Information Systems, vol. 61, no. 1, pp. 485–511, 2019. doi: 10.1007/s10115-018-1283-x
|
[15] |
R. Mortazavi and S. H. Erfani, “GRAM: An efficient (k, l) graph anonymization method,” Expert Systems with Applications, vol. 153, article no. 113454, 2020. doi: 10.1016/j.eswa.2020.113454
|
[16] |
W. L. Ren, K. Ghazinour, and X. Lian, “kt-safety: Graph release via k-anonymity and t-closeness,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9102–9113, 2023. doi: 10.1109/TKDE.2022.3221333
|
[17] |
P. Samarati and L. Sweeney, “Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression,” Available at: https://dataprivacylab.org/dataprivacy/projects/kanonymity/paper3.pdf, 1998.
|
[18] |
M. B. Jamshidi, N. Alibeigi, N. Rabbani, et al., “Artificial neural networks: A powerful tool for cognitive science,” in Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, Canada, pp. 674–679, 2018.
|
[19] |
“Yelp dataset challenge,” Available at: https://datacollaboratives.org/cases/yelp-dataset-challenge.html.
|
[20] |
“Stanford large network dataset collection,” Available at: http://snap.stanford.edu/data/.
|
[21] |
J. Domingo-Ferrer and J. M. Mateo-Sanz, “Practical data-oriented microaggregation for statistical disclosure control,” IEEE Transactions on Knowledge and data Engineering, vol. 14, no. 1, pp. 189–201, 2002. doi: 10.1109/69.979982
|
[22] |
M. Siddula, Y. S. Li, X. Z. Cheng, et al., “Anonymization in online social networks based on enhanced equi-cardinal clustering,” IEEE Transactions on Computational Social Systems, vol. 6, no. 4, pp. 809–820, 2019. doi: 10.1109/TCSS.2019.2928324
|
[23] |
F. Bonchi, A. Gionis, and T. Tassa, “Identity obfuscation in graphs through the information theoretic lens,” Information Sciences, vol. 275, pp. 232–256, 2014. doi: 10.1016/j.ins.2014.02.035
|