Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. PRELIMINARIES
A. Agglomerative Hierarchical Clustering
B. Homomorphic Encryption
III. PROPOSED APPROACH
IV. IMPLEMENTATION
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Lynin Sokhonn [ Department of Electrical and Computer Engineering Inha University ]
• Yun-Soo Park [ Department of Electrical and Computer Engineering Inha University ]
• Mun-Kyu Lee [ Department of Computer Engineering Inha University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004