Abstract
One highly discussed research topic is user privacy protection and the usability of models in data mining tasks. Currently, the most k-means clustering approach using differential privacy is based on trusted third-party servers. However, malicious servers exist in many applications and cause privacy leakages of user data. The Personalized Local Differential Privacy k-means algorithm (PLDP k-means) is proposed in this paper. To satisfy the PLDP mechanism, a perturbation mechanism is used to perturb the user data at the local side. Then clustering is completed by iteration between the local and server sides. The third-party server remains inaccessible to the real user data and considers the users’ personalized privacy demands in the proposed algorithm. In addition, the iterative centroid perturbation algorithm is proposed in this paper for resisting inference attacks and improving the utility of clustering via a privacy budget allocation sequence. Theoretical analysis demonstrates the privacy of the proposed algorithm. Experimental results indicate that the proposed algorithm effectively preserves the utility of clustering while satisfying the PLDP mechanism.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, X., Yang, L.T., Song, L., Wang, H., Ren, L., Deen, M.J.: A tensor-based multiattributes visual feature recognition method for industrial intelligence. IEEE Trans. Ind. Informatics 17(3), 2231–2241 (2021)
Liu, Q., Tian, Y., Wu, J., Peng, T., Wang, G.: Enabling verifiable and dynamic ranked search over outsourced data. IEEE Trans. Serv. Comput. 15(1), 69–82 (2022)
Wang, S., Sun, Y., Bao, Z.: On the efficiency of k-means clustering: evaluation, optimization, and algorithm selection. In: Proceedings of the VLDB Endowment, pp. 163–175 (2020)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Xiao, Y., Xiong, L.: Protecting locations with differential privacy under temporal correlations. In: Proceedings of the ACM Conference on Computer and Communications Security, pp. 1298–1309. ACM, Denver (2015)
Su, D., Cao, J., Li, N., Bertino, E., Lyu, M., Jin, H.: Differentially private k-means clustering and a hybrid approach to private optimization. ACM Trans. Priv. Secur. 20(4), 1–33 (2017)
Nguyen, T.D., Gupta, S., Rana, S., Venkatesh, S.: Privacy aware K-means clustering with high utility. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 388–400. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_31
Bertino, E.: Differentially private k-means clustering. In: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp. 26–37. ACM, New Orleans (2016)
Ni, T., Qiao, M., Chen, Z., Zhang, S., Zhong, H.: Utility-efficient differentially private k-means clustering based on cluster merging. Neurocomputing 424(1), 205–214 (2021)
Ye, Q.Q., Meng, X.F., Zhu, M.J., Huo, Z.: Survey on local differential privacy. J. Softw. 29(7), 1981–2005 (2018)
Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the ACM Conference on Computer and Communications Security, pp. 1054–1067. ACM, Scottsdale (2014)
Cormode, G., Jha, S., Kulkarni, T., Li, N., Srivastava, D., Wang, T.: Privacy at scale: local differential privacy in practice. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1655–1658. ACM, Houston, TX, USA (2018)
Xia, C., Hua, J., Tong, W., Zhong, S.: Distributed k-means clustering guaranteeing local differential privacy. Comput. Secur. 90(1), 1–11 (2020)
Gu, X., Li, M., Xiong, L., Cao, Y.: Providing input-discriminative protection for local differential privacy. In: International Conference on Data Engineering(ICDE), pp. 505–516. IEEE, Dallas, Texas (2016)
Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, pp. 289–300. IEEE, Helsinki, Finland (2016)
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Minimax optimal procedures for locally private estimation. J. Am. Stat. Assoc. 113(521), 182–201 (2018)
Acknowledgements
This research was supported by the National Natural Science Foundation of China under Grant 61801131, and Guangxi Natural Science Foundation under Grant 2022GXNSFAA035632.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Luo, Y., Wang, Z., Zhang, S., Liu, J. (2023). Efficient-Secure k-means Clustering Guaranteeing Personalized Local Differential Privacy. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-22677-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22676-2
Online ISBN: 978-3-031-22677-9
eBook Packages: Computer ScienceComputer Science (R0)