一种基于GDK-means的高校贫困生信息隐私保护方法研究  

Research on a College Information Privacy-Protection Method for Poor Students Based on GDK-means

作　　者：刘晓娜[1] 王恺 王成德[1] 徐彦强 LIU Xiaona;WANG Kai;WANG Chengde;XU Yanqiang(Lanzhou University of Arts and Science,Lanzhou 730000,China;Lanzhou Institute of Technology,Lanzhou 730050,China)

机构地区：[1]兰州文理学院,甘肃兰州730000 [2]兰州工业学院,甘肃兰州730050

出　　处：《现代信息科技》2023年第11期30-33,共4页Modern Information Technology

基　　金：甘肃省高等学校创新基金项目(2020B-256)。

摘　　要：高校在对贫困生的资助过程中,为保证公开、公平,会获取相关学生很多关键性隐私数据,如贫困原因、生源所在地、家庭收入、家庭成员、在校消费等敏感隐私数据。同时资助的结果又要求必须公开以保证管理过程的公正性。针对高校对贫困生数据发布中的公开与隐私保护之间的矛盾,提出了一种基于GDK-means的隐私保护方法。在该算法下,在K-means聚类的基础上,对生成的簇进行簇内泛化,来对发布的敏感数据进行去隐私化处理,以达到用户隐私保护的目的,同时量化了处理所带来的信息丢失度。经理论分析和实验,验证了采用GDK-means算法,在保证数据可用性的前提下,可实现数据发布中较好的隐私保护性。In the process of providing financial aid to poor students,colleges and universities obtain a lot of key private data about the students in order to ensure openness and fairness,such as the reason for poverty,the location of the student's origin,family income,family members,school spending and other sensitive private data.At the same time,the results of financial aid must be made public to ensure the fairness of the management process.A privacy-protection method based on GDK-means is proposed to address the contradiction between disclosure and privacy-protection in the release of data on poor students in colleges and universities.Under this algorithm,the published sensitive data can be de-privatised by intra-cluster generalisation of the generated clusters on the basis of K-means clustering to achieve the purpose of user privacy-protection,while quantifying the degree of information loss caused by the processing.After theoretical analysis and experiments,it is verified that the use of GDK-means algorithm can achieve better privacy-protection in data publishing under the premise of ensuring data availability.

关 键 词：贫困生资助 分布式聚类 隐私保护 K-MEANS算法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G647[自动化与计算机技术—计算机科学与技术]