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 …
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
The cost of privacy: Optimal rates of convergence for parameter estimation with differential
privacy Page 1 The Annals of Statistics 2021, Vol. 49, No. 5, 2825–2850 https://doi.org/10.1214/21-AOS2058 …
privacy Page 1 The Annals of Statistics 2021, Vol. 49, No. 5, 2825–2850 https://doi.org/10.1214/21-AOS2058 …
Optimal learners for realizable regression: Pac learning and online learning
In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …
the PAC learning setting and the online learning setting. Previous work had established the …
Finite sample differentially private confidence intervals
We study the problem of estimating finite sample confidence intervals of the mean of a
normal population under the constraint of differential privacy. We consider both the known …
normal population under the constraint of differential privacy. We consider both the known …
Privately learning high-dimensional distributions
We present novel, computationally efficient, and differentially private algorithms for two
fundamental high-dimensional learning problems: learning a multivariate Gaussian and …
fundamental high-dimensional learning problems: learning a multivariate Gaussian and …
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
We study and provide instance-optimal algorithms in differential privacy by extending and
approximating the inverse sensitivity mechanism. We provide two approximation …
approximating the inverse sensitivity mechanism. We provide two approximation …
DPPro: Differentially private high-dimensional data release via random projection
Releasing representative data sets without compromising the data privacy has attracted
increasing attention from the database community in recent years. Differential privacy is an …
increasing attention from the database community in recent years. Differential privacy is an …
Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia
It is known, that the polluted air influences straightforwardly on human wellbeing. Along
these lines, the air quality checking surveys the nature of air and recognize defiled …
these lines, the air quality checking surveys the nature of air and recognize defiled …
Differential privacy and federal data releases
JP Reiter - Annual review of statistics and its application, 2019 - annualreviews.org
Federal statistics agencies strive to release data products that are informative for many
purposes, yet also protect the privacy and confidentiality of data subjects' identities and …
purposes, yet also protect the privacy and confidentiality of data subjects' identities and …
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
YX Wang - arXiv preprint arXiv:1803.02596, 2018 - arxiv.org
We revisit the problem of linear regression under a differential privacy constraint. By
consolidating existing pieces in the literature, we clarify the correct dependence of the …
consolidating existing pieces in the literature, we clarify the correct dependence of the …