A New Method of Privacy Protection: Random k-Anonymous

IEEE Access

Mznah Al-Rodhaan¹  Yuan Tian²  Tinghuai Ma³  Fagen Song³ 

[1] Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;Nanjing Institute of Technology, Nanjing, China;School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing, China;

关键词: Differential privacy;    information security;    k-anonymous;    privacy protection;    random k-anonymous;   

DOI  :  10.1109/ACCESS.2019.2919165

来源: DOAJ

【 摘 要 】

A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.

【 授权许可】

Unknown