AQ-DP: A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data

Abstract

The rapid development of big data has brought great convenience to human's lives. The circulation and sharing of information are two main characteristics of the big data era. However, the risk of privacy leakage is also greatly increased when we enjoy the various services of big data. Therefore, how to protect the data privacy in the complex context of big data has become a research hotspot in academic circles. Most of the current researches on privacy protection are divided into two research fields: k-anonymity and differential privacy. Some existing research shows that traditional methods of privacy protection, such as k-anonymity and its extension, cannot achieve absolutely security. The emergence of differential privacy provides a new solution for privacy protection. We draw the lessons from exiting work and propose a new privacy method based on differential privacy: AQ-DP. We propose the first method for classifying quasi-identifiers based on sensitive attributes, which divide quasi-identifiers into associated quasi-identifiers (AQI) and non-associated quasi-identifiers (NAQI). The purpose is not to lose the correlation between quasi-identifiers and sensitive attributes. Our model AQ-DP carries out random shuffling of NAQls., generalizes the AQIs., and adds random noise that satisfies the laplacian distribution to the statistics. We have conducted extensive experiments, confirming that our model can achieve a satisfying privacy level and data utility.