Improved Privacy-Preserving Clustering Model for Distributed Data

O. M. Omowaye; V. I. E. Anireh; N. D. Nwiabu

doi:https://doi.org/10.47001/JAIET/2026.308005

Authors

O. M. Omowaye

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

V. I. E. Anireh

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

N. D. Nwiabu

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

Abstract

Privacy-preserving distributed clustering lets organisations analyse data held in separate locations without centralising raw records. Homomorphic encryption can protect centroid exchange, but distributed k-means becomes slow and communication-intensive when every centroid feature is encrypted and transmitted separately. This article presents a CKKS-based distributed k-means method that represents each centroid as a packed ciphertext. Under a semi-honest coordinator assumption, participant records remain local, centroid updates are encrypted before transmission, and aggregation is performed on ciphertexts. The method was evaluated on Iris, Wine, Diabetes, and Credit Card Default datasets under simulated 3-participant and 5-participant settings. Three configurations were compared: centralised plaintext k-means, a per-dimension CKKS encrypted centroid baseline, and the packed CKKS method. Clustering quality was measured using silhouette score and Davies-Bouldin index, while efficiency was measured using encryption time, total execution time, and encrypted communication volume. Compared with the per-dimension encrypted baseline, the packed method reduced encryption time by 57.47% to 95.27% and encrypted communication volume by 75.00% to 95.65%. In the Credit Card Default 3-participant setting, total runtime fell from 24.08 seconds to 1.12 seconds, and encrypted communication fell from 450,504.18 KB to 19,587.03 KB. The silhouette-score difference relative to the encrypted baseline was 0.0000 in all reported encrypted comparisons, indicating that centroid packing reduced operational cost without adding clustering-quality loss.

Keywords

Privacy-preserving clustering distributed clustering CKKS homomorphic encryption ciphertext packing k-means clustering encrypted aggregation

Citation of this Article

O. M. Omowaye, V. I. E. Anireh, & N. D. Nwiabu. (2026). Improved Privacy-Preserving Clustering Model for Distributed Data. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(8), 41-52. Article DOI: https://doi.org/10.47001/JAIET/2026.308005

References

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[2] Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.

[3] Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2024). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 36(2), 559-578.

[4] Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.

[5] Cheon, J. H., Kim, A., Kim, M., & Song, Y. (2017). Homomorphic encryption for arithmetic of approximate numbers. Advances in Cryptology - ASIACRYPT 2017, 409-437.

[6] Pan, Y., Chao, Z., He, W., & Xue, M. (2024). FedSHE: Privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption. Cybersecurity, 7(1), 40.

[7] Zuo, Z., Su, N., Li, B., & Zhang, T. (2024). Pack: Towards communication-efficient homomorphic encryption in federated learning. Proceedings of the 2024 ACM Symposium on Cloud Computing, 470-486. https://doi.org/10.1145/3698038.3698557

[8] Chen, F., Dong, J., Hu, X., Dong, Z., & Dai, W. (2024). HI-CKKS: Is high-throughput neglected? Reimagining CKKS efficiency with parallelism. Cryptology ePrint Archive, Paper 2024/1976. https://eprint.iacr.org/2024/1976

[9] Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. Proceedings of the 41st Annual ACM Symposium on Theory of Computing, 169-178.

[10] Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascon, A., Ghazi, B., Gibbons, P. B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 17(1-2), 1-210.

[11] Kumar, S., Gupta, R., & Singh, A. (2023). Configurable encryption and decryption architectures for CKKS-based homomorphic encryption. Sensors, 23(17), 7389.

[12] Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.

[13] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175-1191.

[14] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.), Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 281-297). University of California Press.

[15] Garst, S., & Reinders, M. (2024). Federated K-means clustering. In Proceedings of the International Conference on Pattern Recognition (ICPR 2024), Lecture Notes in Computer Science (Vol. 15302). Springer.

[16] Luo, G., Chen, N., He, J., Jin, B., Zhang, Z., & Li, Y. (2024). Privacy-preserving clustering federated learning for non-IID data. Future Generation Computer Systems, 161, 395-410.

[17] Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.

[18] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.