Privacy and communication constraints are two major bottlenecks in federated learning (FL)
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …

We study the mean estimation problem under communication and local differential privacy
constraints. While previous work has proposed order-optimal algorithms for the same …

We revisit the problem of designing scalable protocols for private statistics and private
federated learning when each device holds its private data. Locally differentially private …

In this work, we study practical heuristics to improve the performance of prefix-tree based
algorithms for differentially private heavy hitter detection. Our model assumes each user has …

Abundant valuable knowledge can be obtained by learning frequencies of relations in a
decentralized network, which benefits various further complex tasks, such as range query …

Collecting and analyzing evolving longitudinal data has become a common practice. One
possible approach to protect the users' privacy in this context is to use local differential …

We investigate the contraction properties of locally differentially private mechanisms. More
specifically, we derive tight upper bounds on the divergence between Pand Qoutput …

We investigate the strong data processing inequalities of locally differentially private
mechanisms under a specific f-divergence, namely the E γ-divergence. More specifically, we …

We consider a private discrete distribution estimation problem with one-bit communication
constraint. The privacy constraints are imposed with respect to the local differential privacy …

In this paper, we propose a new class of local differential privacy (LDP) schemes based on
combinatorial block designs for a discrete distribution estimation. This class not only …