A comprehensive survey on local differential privacy toward data statistics and analysis
T Wang, X Zhang, J Feng, X Yang - Sensors, 2020 - mdpi.com
Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …
Private and polynomial time algorithms for learning Gaussians and beyond
H Ashtiani, C Liaw - Conference on Learning Theory, 2022 - proceedings.mlr.press
We present a fairly general framework for reducing $(\varepsilon,\delta) $-differentially
private (DP) statistical estimation to its non-private counterpart. As the main application of …
private (DP) statistical estimation to its non-private counterpart. As the main application of …
A private and computationally-efficient estimator for unbounded gaussians
We give the first polynomial-time, polynomial-sample, differentially private estimator for the
mean and covariance of an arbitrary Gaussian distribution $ N (\mu,\Sigma) $ in $\R^ d $. All …
mean and covariance of an arbitrary Gaussian distribution $ N (\mu,\Sigma) $ in $\R^ d $. All …
Friendlycore: Practical differentially private aggregation
Differentially private algorithms for common metric aggregation tasks, such as clustering or
averaging, often have limited practicality due to their complexity or to the large number of …
averaging, often have limited practicality due to their complexity or to the large number of …
Differentially private clustering: Tight approximation ratios
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 …
including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient …
Differential privacy from locally adjustable graph algorithms: k-core decomposition, low out-degree ordering, and densest subgraphs
Differentially private algorithms allow large-scale data analytics while preserving user
privacy. Designing such algorithms for graph data is gaining importance with the growth of …
privacy. Designing such algorithms for graph data is gaining importance with the growth of …
On the sample complexity of privately learning unbounded high-dimensional gaussians
We provide sample complexity upper bounds for agnostically learning multivariate
Gaussians under the constraint of approximate differential privacy. These are the first finite …
Gaussians under the constraint of approximate differential privacy. These are the first finite …
Locally private k-means clustering
U Stemmer - Journal of Machine Learning Research, 2021 - jmlr.org
We design a new algorithm for the Euclidean k-means problem that operates in the local
model of differential privacy. Unlike in the non-private literature, differentially private …
model of differential privacy. Unlike in the non-private literature, differentially private …
Improving federated learning face recognition via privacy-agnostic clusters
The growing public concerns on data privacy in face recognition can be greatly addressed
by the federated learning (FL) paradigm. However, conventional FL methods perform poorly …
by the federated learning (FL) paradigm. However, conventional FL methods perform poorly …
Locally private k-means in one round
We provide an approximation algorithm for k-means clustering in the\emph {one-
round}(aka\emph {non-interactive}) local model of differential privacy (DP). Our algorithm …
round}(aka\emph {non-interactive}) local model of differential privacy (DP). Our algorithm …