다중 클라이언트 환경에서 동형 암호를 이용한 프라이버시 보장형 K-평균 클러스터링
Privacy-Preserving K-means Clustering using Homomorphic Encryption in a Multiple Clients Environ
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초록
기계 학습은 다양한 현상의 예측 및 분석 등을 가장 정확하게 수행하는 기술 중 하나이다. K-평균 클러스터링은 주어진 데이터들을 비슷한 데이터들의 군집으로 분류하는 기계 학습 기법의 한 종류로 다양한 분야에서 사용된다. K-평균 클러스터링의 성능을 높이기 위해서는 가능하면 많은 데이터에 기반한 분석을 수행하는 것이 바람직하므로, K-평균 클러스터링은 데이터를 제공하는 다수의 클라이언트들과 제공받은 데이터들을 사용하여 클러스터의 중심값을 계산하는 서버가 있는 모델에서 수행될 수 있다. 그러나 이 모델은 클라이언트들의 데이터가 민감한 정보를 포함하고 있는 경우, 서버가 클라이언트들의 프라이버시를 침해할 수 있다는 문제점이 있다. 본 논문에서는 다수의 클라이언트가 있는 모델에서 이러한 문제를 해결하기 위해 동형 암호를 사용하여 클라이언트의 프라이버시를 보호하며 기계 학습을 수행할 수 있는 프라이버시 보장형 K-평균 클러스터링 방법을 제안한다.
Abstract
Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients’ data are associated with private information, the server can infringe clients’ privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.
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