Synthetic Data--what, why and how?
This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …
expanding work on synthetic data technologies, with a particular focus on privacy. The …
Pioneering new paths: the role of generative modelling in neurological disease research
Recently, deep generative modelling has become an increasingly powerful tool with seminal
work in a myriad of disciplines. This powerful modelling approach is supposed to not only …
work in a myriad of disciplines. This powerful modelling approach is supposed to not only …
Noise-aware statistical inference with differentially private synthetic data
While generation of synthetic data under differential privacy (DP) has received a lot of
attention in the data privacy community, analysis of synthetic data has received much less …
attention in the data privacy community, analysis of synthetic data has received much less …
Mitigating statistical bias within differentially private synthetic data
Increasing interest in privacy-preserving machine learning has led to new and evolved
approaches for generating private synthetic data from undisclosed real data. However …
approaches for generating private synthetic data from undisclosed real data. However …
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn
population level statistics, but pooling the sensitive data sets is not possible. We propose a …
population level statistics, but pooling the sensitive data sets is not possible. We propose a …
Collaborative learning from distributed data with differentially private synthetic data
Background Consider a setting where multiple parties holding sensitive data aim to
collaboratively learn population level statistics, but pooling the sensitive data sets is not …
collaboratively learn population level statistics, but pooling the sensitive data sets is not …
Bayesian inference for inflation volatility modeling in Ghana
Purpose The purpose of this paper is to emphasize the risks involved in modeling inflation
volatility in the context of macroeconomic policy. For countries like Ghana that are always …
volatility in the context of macroeconomic policy. For countries like Ghana that are always …
Debiasing Synthetic Data Generated by Deep Generative Models
While synthetic data hold great promise for privacy protection, their statistical analysis poses
significant challenges that necessitate innovative solutions. The use of deep generative …
significant challenges that necessitate innovative solutions. The use of deep generative …
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
The evaluation of synthetic data generation is crucial, especially in the retail sector where
data accuracy is paramount. This paper introduces a comprehensive framework for …
data accuracy is paramount. This paper introduces a comprehensive framework for …
Privacy-Protected Spatial Autoregressive Model
D Huang, Z Kong, S Wu, H Wang - arxiv preprint arxiv:2403.16773, 2024 - arxiv.org
Spatial autoregressive (SAR) models are important tools for studying network effects.
However, with an increasing emphasis on data privacy, data providers often implement …
However, with an increasing emphasis on data privacy, data providers often implement …