基于差分隐私的K-means算法优化研究综述

Review of K-means Algorithm Optimization Based on Differential Privacy

差分隐私K-means算法(Differential Privacy K-means Algorithm,DP K-means)作为一种基于差分隐私技术的隐私保护数据挖掘(Privacy Preserving Data Mining,PPDM)模型,因简单高效且可保障数据的隐私而备受研究者的关注。文中首先阐述了差分隐私K-means算法的原理、隐私攻击模型,以分析算法的不足。然后从数据预处理、隐私预算分配、聚簇划分等3个角度讨论分析DP K-means算法改进研究的优缺点,并对研究中的相关数据集和通用评价指标进行了总结。最后指出DP K-means算法改进研究中亟待解决的挑战性问题,并展望了DP K-means算法的未来发展趋势。

Differential privacy K-means algorithm(DP K-means),as a kind of privacy preserving data mining(PPDM)model based on differential privacy technology,has attracted much attention from researchers because of its simplicity,efficiency and ability to guarantee data privacy.Firstly,the principle and privacy attack model of differential privacy K-means Algorithm are described,and the shortcomings of the algorithm are analyzed.Then,the advantages and disadvantages of the improvement research of DP K-means algorithm are discussed and analyzed from three perspectives,including data preprocessing,privacy budget allocation and cluster partition,and the relevant data sets and common evaluation indexes in the research are summarized.At last,the challenging problems to be solved in the improvement research of DP K-means algorithm are pointed out,and the future development trend of DP K-means algorithm is prospected.