Survey on the Applications of Differential Privacy

In the era of big data and information explosion, the issue of personal information privacy protection has garnered increasing attention, leading to the emergence of various privacy protection schemes. Among these, differential privacy technology stands out due to its rigorous mathematical foundation and the ability to quantify privacy protection levels conveniently. Since its introduction by Dwork et al. in 2006, differential privacy has become a pivotal approach for addressing privacy concerns across a wide range of big data applications. To facilitate the flexible application of differential privacy in emerging domains, this paper provides a comprehensive overview of the existing research on differential privacy in the fields of data mining, and machine learning. We summarize the application frameworks, as well as the advantages and limitations, of differential privacy in these contexts. Finally, potential future directions for advancements in differential privacy technology are discussed.