earticle

영어

Hierarchical clustering is a widely-used technique in data analysis. Typically, tools for this method operate on data that is in its original, readable form. This poses privacy concerns when dealing with sensitive data that needs to remain confidential. To tackle this issue, we developed a method that integrates CKKS homomorphic encryption, allowing the clustering process to happen without revealing the raw data. However, a challenge emerges when trying to sort the encrypted distances, a crucial step for single linkage clustering. Given the complexities of sorting encrypted data, we propose a cooperative approach: the data owner aids in the sorting process and shares a list of data positions. Using this list, the server can determine how data points cluster together. Our approach ensures a secure hierarchical single linkage clustering process, grouping data without exposing its original content.