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Found 16 papers, 5 papers with code
Regulation Games for Trustworthy Machine Learning
Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy.
Personalized Differential Privacy for Ridge Regression
DP requires to specify a uniform privacy level $\varepsilon$ that expresses the maximum privacy loss that each data point in the entire dataset is willing to tolerate.
Memorization in Self-Supervised Learning Improves Downstream Generalization
Our definition compares the difference in alignment of representations for data points and their augmented views returned by both encoders that were trained on these data points and encoders that were not.
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Differential privacy and randomized smoothing are effective defenses that provide certifiable guarantees for each of these threats, however, it is not well understood how implementing either defense impacts the other.
Have it your way: Individualized Privacy Assignment for DP-SGD
DP-SGD is the canonical approach to training models with differential privacy.
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility
Deploying machine learning (ML) models often requires both fairness and privacy guarantees.
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
FL is promoted as a privacy-enhancing technology (PET) that provides data minimization: data never "leaves" personal devices and users share only model updates with a server (e. g., a company) coordinating the distributed training.
Introducing Model Inversion Attacks on Automatic Speaker Recognition
To the best of our knowledge, our work is the first one extending MI attacks to audio data, and our results highlight the security risks resulting from the extraction of the biometric data in that setup.
Dataset Inference for Self-Supervised Models
We introduce a new dataset inference defense, which uses the private training set of the victim encoder model to attribute its ownership in the event of stealing.
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees
Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP).
When the Curious Abandon Honesty: Federated Learning Is Not Private
Instead, these devices share gradients, parameters, or other model updates, with a central party (e. g., a company) coordinating the training.
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
An important problem in deep learning is the privacy and security of neural networks (NNs).
A Systematic Review on Model Watermarking for Neural Networks
Machine learning (ML) models are applied in an increasing variety of domains.
Testing Robustness Against Unforeseen Adversaries
To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks.
Tracking all members of a honey bee colony over their lifetime
Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions.
