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Found 5 papers, 5 papers with code
Assessing Differentially Private Variational Autoencoders under Membership Inference
We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders.
On the privacy-utility trade-off in differentially private hierarchical text classification
This work investigates the privacy-utility trade-off in hierarchical text classification with differential privacy guarantees, and identifies neural network architectures that offer superior trade-offs.
Quantifying identifiability to choose and audit $ε$ in differentially private deep learning
We transform $(\epsilon,\delta)$ to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset.
Assessing differentially private deep learning with Membership Inference
We empirically compare local and central differential privacy mechanisms under white- and black-box membership inference to evaluate their relative privacy-accuracy trade-offs.
Reconstruction and Membership Inference Attacks against Generative Models
We present two information leakage attacks that outperform previous work on membership inference against generative models.
