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 …
M Wang, H Jiang, P Peng, Y Li - IEEE Transactions on Mobile …, 2023 - ieeexplore.ieee.org
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 …
S Asoodeh, H Zhang - IEEE Journal on Selected Areas in …, 2024 - ieeexplore.ieee.org
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 …
SH Nam, VYF Tan, SH Lee - IEEE Transactions on Information …, 2024 - ieeexplore.ieee.org
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 …
HY Park, SH Nam, SH Lee - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
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 …