Differential Attribute Desensitization System for Personal Information Protection
Peng Jia, Xin Huang, Min Li, Jiacheng Zhang, Yifei Zhang, Neng Gao · 2019
Big data brings much convenience to our daily life, while it also raises concerns to personal information leakage. It is important for data owner to protect sensitive data from being attacked. In this paper, we elaborate on the possible attacks that may be suffered at various stages of personal data life cycle and the corresponding privacy protection methods. Based on these methods, a differential attribute desensitization system (DADS) for personal information protection has been proposed. Data owner can define sensitive attribute level in the DADS. And then DADS can automatically identify sensitive data, and take differentiated data desensitization measures for structured and unstructured multi-attribute information. Experimental results show that DADS can automatically adapt to different types of sensitive information and protect personal sensitive information effectively.