<ul><li>Clustering is a fundamental data processing task used for grouping records based on one or more features.</li><li>In the vertically partitioned setting, computing distances between records requires access to all distributed features, which may be privacy-sensitive and cannot be directly shared.</li><li>A novel solution based on homomorphic encryption and differential privacy (DP) is proposed, reducing communication complexity and ensuring privacy with minimal impact on utility.</li><li>The proposed solution allows for practical deployments even in WAN settings, maintaining accuracy comparable to plaintext k-means algorithms.</li></ul>

Privacy-Preserving Vertical K-Means Clustering

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