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쿼드 트리를 이용한 동적 공간 분할 기반 차분 프라이버시 k-평균 클러스터링 알고리즘
구한준(Hanjun Goo),정우환(Woohwan Jung),오성웅(Seongwoong Oh),권수용(Suyong Kwon),심규석(Kyuseok Shim) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.3
There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker’s background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.