The collection of user attributes by service providers is a double-edged sword. They are
instrumental in driving statistical analysis to train more accurate predictive models like …

We consider the problem of computing differentially private approximate histograms and
heavy hitters in a stream of elements. In the non-private setting, this is often done using the …

Frequency estimation mechanisms are widely applied in domains such as machine learning
and cloud computing, where it is desirable to provide statistical information. As a …

Mining frequent items from high-dimensional historical data (set-valued data) from massive
users can extract the most routine data, playing a vital role in data mining. However, frequent …

[目的] 本地化差分隐私是优秀的隐私保护模型, 能够在数据共享, 发布的场景下对群体进行统计
分析, 保护个人数据隐私. 本文围绕本地化差分隐私进行综述, 为未来工作提供参考.[文献范围] …

This thesis addresses several challenges in efficiently processing large datasets and in
privacy-preserving computation. Specifically, in this thesis, we look into the following …

In this thesis we study differential privacy. Differential privacy allows us to give formal privacy
guarantees of mechanisms for statistical query release by quantifying privacy loss. However …

[Objective] This paper systematically introduces local differential privacy and provides a
reference for the protection of personal data privacy under data sharing and …