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Found 10 papers, 3 papers with code
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database.
Complex-valued deep learning with differential privacy
We present $\zeta$-DP, an extension of differential privacy (DP) to complex-valued functions.
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
$\psi$ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation.
An automatic differentiation system for the age of differential privacy
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML).
Partial sensitivity analysis in differential privacy
However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss.
NeuralDP Differentially private neural networks by design
The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigorous privacy guarantees to the individual.
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models.
Differentially private federated deep learning for multi-site medical image segmentation
The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.
