Cross-institutional collaborative risk control aims to achieve risk pattern sharing among entities such as banks, consumer finance companies, payment providers, and guarantee companies. However, directly aggregating raw samples can lead to constraints related to compliance, competition, and data sovereignty. To address this contradiction, this paper proposes a homomorphic encryption-based privacy-preserving k-means clustering method for risk control scenarios. Homomorphic encryption is used to calculate cross-institutional ciphertext distances and update cluster centers. Batch encoding and ciphertext pipelined processing reduce rounds and communication costs, creating a closed loop for clustering, profiling, and early warning in the risk control application layer. The paper investigates four aspects: ciphertext similarity calculation, cross-institutional center aggregation, convergence judgment, and risk cluster interpretation, and presents a deployable distributed system architecture. A comparative analysis of experimental results from publicly available literature over the past three years shows that using homomorphic encryption-based clustering methods can significantly reduce the risk of plaintext leakage during collaboration between different institutions while ensuring controllable accuracy loss. Furthermore, it achieves relatively low communication time and latency requirements even with large datasets. The above research shows that applying the privacy-preserving k-means algorithm to pre-loan customer segmentation, mid-loan abnormal group identification, and post-loan collection strategy allocation can provide a safe and scalable technical approach for joint risk control.

Ziting Mai. Research on the Design and Application of Homomorphic Encryption Privacy-Preserving k-means Clustering Algorithm for Cross-Institutional Collaborative Risk Control. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 73-81. https://doi.org/10.38007/IJMC.2026.070109

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