With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

We present a new locally differentially private algorithm for the heavy hitters problem that
achieves optimal worst-case error as a function of all standardly considered parameters …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

Abstract k-means clustering is widely used in many fields such as data mining, machine
learning, and information retrieval. In many cases, users need to cooperate to perform k …

We study the task of differentially private clustering. For several basic clustering problems,
including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient …

Privacy-preserving data analysis is an emerging area that addresses the dilemma of
performing data analysis on user data while protecting users' privacy. In this paper, we …