A comprehensive survey on local differential privacy
X Xiong, S Liu, D Li, Z Cai, X Niu - Security and Communication …, 2020 - Wiley Online Library
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 …
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Private hypothesis selection
We provide a differentially private algorithm for hypothesis selection. Given samples from an
unknown probability distribution $ P $ and a set of $ m $ probability distributions $\mathcal …
unknown probability distribution $ P $ and a set of $ m $ probability distributions $\mathcal …
The role of interactivity in local differential privacy
We study the power of interactivity in local differential privacy. First, we focus on the
difference between fully interactive and sequentially interactive protocols. Sequentially …
difference between fully interactive and sequentially interactive protocols. Sequentially …
Arboretum: A planner for large-scale federated analytics with differential privacy
E Margolin, K Newatia, T Luo, E Roth… - Proceedings of the 29th …, 2023 - dl.acm.org
Federated analytics is a way to answer queries over sensitive data that is spread across
multiple parties, without sharing the data or collecting it in a single place. Prior work has …
multiple parties, without sharing the data or collecting it in a single place. Prior work has …
Differentially private assouad, fano, and le cam
Abstract Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used
techniques to prove lower bounds for statistical estimation tasks. We propose their …
techniques to prove lower bounds for statistical estimation tasks. We propose their …
Average-case averages: Private algorithms for smooth sensitivity and mean estimation
The simplest and most widely applied method for guaranteeing differential privacy is to add
instance-independent noise to a statistic of interest that is scaled to its global sensitivity …
instance-independent noise to a statistic of interest that is scaled to its global sensitivity …
On robustness and local differential privacy
On robustness and local differential privacy Page 1 The Annals of Statistics 2023, Vol. 51, No.
2, 717–737 https://doi.org/10.1214/23-AOS2267 © Institute of Mathematical Statistics, 2023 …
2, 717–737 https://doi.org/10.1214/23-AOS2267 © Institute of Mathematical Statistics, 2023 …
Unbiased statistical estimation and valid confidence intervals under differential privacy
We present a method for producing unbiased parameter estimates and valid confidence
intervals under the constraints of differential privacy, a formal framework for limiting …
intervals under the constraints of differential privacy, a formal framework for limiting …
A primer on private statistics
Differentially private statistical estimation has seen a flurry of developments over the last
several years. Study has been divided into two schools of thought, focusing on empirical …
several years. Study has been divided into two schools of thought, focusing on empirical …