Collecting and analyzing multidimensional data with local differential privacy
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …
Optimal schemes for discrete distribution estimation under locally differential privacy
We consider the minimax estimation problem of a discrete distribution with support size k
under privacy constraints. A privatization scheme is applied to each raw sample …
under privacy constraints. A privatization scheme is applied to each raw sample …
Locally private graph neural networks
S Sajadmanesh, D Gatica-Perez - … of the 2021 ACM SIGSAC conference …, 2021 - dl.acm.org
Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …
representations for various graph inference tasks. However, learning over graph data can …
Federated heavy hitters discovery with differential privacy
The discovery of heavy hitters (most frequent items) in user-generated data streams drives
improvements in the app and web ecosystems, but can incur substantial privacy risks if not …
improvements in the app and web ecosystems, but can incur substantial privacy risks if not …
Locally differentially private heavy hitter identification
The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions
while preserving their privacy. The basic LDP frequency oracle protocol enables the …
while preserving their privacy. The basic LDP frequency oracle protocol enables the …
Differentially private testing of identity and closeness of discrete distributions
We study the fundamental problems of identity testing (goodness of fit), and closeness
testing (two sample test) of distributions over $ k $ elements, under differential privacy. While …
testing (two sample test) of distributions over $ k $ elements, under differential privacy. While …
[HTML][HTML] Add noise to remove noise: Local differential privacy for feature selection
Feature selection has become significantly important for data analysis. It selects the most
informative features describing the data to filter out the noise, complexity, and over-fitting …
informative features describing the data to filter out the noise, complexity, and over-fitting …
Test without trust: Optimal locally private distribution testing
We study the problem of distribution testing when the samples can only be accessed using a
locally differentially private mechanism and focus on two representative testing questions of …
locally differentially private mechanism and focus on two representative testing questions of …
Towards sparse federated analytics: Location heatmaps under distributed differential privacy with secure aggregation
We design a scalable algorithm to privately generate location heatmaps over decentralized
data from millions of user devices. It aims to ensure differential privacy before data becomes …
data from millions of user devices. It aims to ensure differential privacy before data becomes …
Linear queries estimation with local differential privacy
R Bassily - The 22nd International Conference on Artificial …, 2019 - proceedings.mlr.press
We study the problem of estimating a set of d linear queries with respect to some unknown
distribution p over a domain $[J] $ based on a sensitive data set of n individuals under the …
distribution p over a domain $[J] $ based on a sensitive data set of n individuals under the …