An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

作     者：Linlin Yuan Tiantian Zhang Yuling Chen Yuxiang Yang Huang Li 

作者机构：State Key Laboratory of Public Big DataCollege of Computer Science and TechnologyGuizhou UniversityGuiyang550025China College of Information EngineeringGuizhou Open UniversityGuiyang550025China Guizhou Academy of Tobacco ScienceGuiyang550025China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第4期

页      面：1561-1579页

核心收录：

学科分类：08[工学] 0839[工学-网络空间安全] 0805[工学-材料科学与工程（可授工学、理学学位）] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Foundation of National Natural Science Foundation of China(62202118) Scientific and Technological Research Projects from Guizhou Education Department(003) Guizhou Provincial Department of Science and Technology Hundred Levels of Innovative Talents Project(GCC018) Top Technology Talent Project from Guizhou Education Department(073) 

主　　题：Blockchain big data K-anonymity 2-means clustering greedy algorithm mean-center method 

摘      要：The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more *** K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big ***,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data *** addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be *** on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data ***,we construct a new information loss function based on the information quantity *** that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss *** addition,to reduce information loss,we improve K-anonymity in two ***,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering *** addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of ***,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information ***,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.