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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.
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.
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.
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.
