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Differentially Private Statistical Inference through -Divergence One Posterior Sampling
Differential privacy guarantees allow the results of a statistical analysis involving sensitive
data to be released without compromising the privacy of any individual taking part …
data to be released without compromising the privacy of any individual taking part …
DP-Fast MH: Private, fast, and accurate Metropolis-Hastings for large-scale Bayesian inference
Bayesian inference provides a principled framework for learning from complex data and
reasoning under uncertainty. It has been widely applied in machine learning tasks such as …
reasoning under uncertainty. It has been widely applied in machine learning tasks such as …
Statistic selection and MCMC for differentially private Bayesian estimation
This paper concerns differentially private Bayesian estimation of the parameters of a
population distribution, when a noisy statistic of a sample from that population is shared to …
population distribution, when a noisy statistic of a sample from that population is shared to …
Differentially private online Bayesian estimation with adaptive truncation
In this paper, a novel online and adaptive truncation method is proposed for differentially
private Bayesian online estimation of a static parameter regarding a population. A local …
private Bayesian online estimation of a static parameter regarding a population. A local …
[PDF][PDF] Robustness-Privacy Trade-Off In Bayesian Neural Networks
M Ghitu, M Wicker, W Knottenbelt - 2024 - imperial.ac.uk
Substantial developments have recently been made to devise provable methods that ensure
the trustworthiness of deep neural networks. Most of these pieces of work study properties …
the trustworthiness of deep neural networks. Most of these pieces of work study properties …
[PDF][PDF] Differentially private and distributed Bayesian learning
M Heikkilä - 2023 - mixheikk.github.io
Abstract Machine learning aims to learn patterns from data. When the data are about people,
a machine learning model will learn information about people. Such models can be used by …
a machine learning model will learn information about people. Such models can be used by …
Monte Carlo Methods For Data Privacy Applications
B Alparslan - 2023 - research.sabanciuniv.edu
This thesis focuses on data privacy applications with Bayesian inference, particularly Markov
chain Monte Carlo (MCMC) methods for two main data privacy problems. Firstly, we focus on …
chain Monte Carlo (MCMC) methods for two main data privacy problems. Firstly, we focus on …