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.
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This research was supported by the National Natural Science Foundation of China under Grant 61801131, and Guangxi Natural Science Foundation under Grant 2022GXNSFAA035632.
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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
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