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Found 5 papers, 3 papers with code
Understanding Practical Membership Privacy of Deep Learning
We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models. We focus on understanding the properties of data sets and samples that make them vulnerable to membership inference.
Privacy-Aware Document Visual Question Answering
We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
PyVBMC: Efficient Bayesian inference in Python
PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020).
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models.
Individual Privacy Accounting with Gaussian Differential Privacy
In order to account for the individual privacy losses in a principled manner, we need a privacy accountant for adaptive compositions of randomised mechanisms, where the loss incurred at a given data access is allowed to be smaller than the worst-case loss.
