Abstract
Spectral clustering is a widely used clustering algorithm based on the advantages of simple implementation, small computational cost, and good adaptability to arbitrarily shaped data sets. However, due to the lack of data protection mechanism in spectral clustering algorithm and the fact that the processed data often contains a large amount of sensitive user information, thus an existing risk of privacy leakage. To address this potential risk, a spectral clustering algorithm based on differential privacy protection is proposed in this paper, which uses the Laplace mechanism to add noise to the input data perturbing the original data information, and then perform spectral clustering, so as to achieve the purpose of privacy protection. Experiments show that the algorithm has both stability and usability, can correctly complete the clustering task with a small loss of accuracy, and can prevent reconstruction attacks, greatly reduce the risk of sensitive information leakage, and effectively protect the model and the original data.
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Cui, Y., Wu, H., Zhang, Y., Gao, Y., Wu, X. (2022). A Spectral Clustering Algorithm Based on Differential Privacy Preservation. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_25
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