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 …

Nowadays, privacy protection has become an important issue in data mining. k-means
clustering and kNN classification are two popular data mining algorithms, which have been …

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 …

Federated learning is generally used in tasks where labels are readily available (eg, next
word prediction). Relaxing this constraint requires design of unsupervised learning …

K-means clustering is a very classical clustering algorithm, and it is also one of the
representatives of unsupervised learning. It has the advantages of a simple idea, high …

It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …

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 …

The shuffle model of differential privacy has attracted attention in the literature due to it being
a middle ground between the well-studied central and local models. In this work, we study …

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 …

In many cases, a service provider might require to aggregate data from end-users to perform
mining tasks such as K-means clustering. Nevertheless, since such data often contain …