208 citations · 588 across the 22 of their papers we have counts for
9 papers · 1 filter
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Leonard Berrada, Soham De, Judy Hanwen Shen +6
Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework…
Differentially Private Diffusion Models Generate Useful Synthetic Images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal +7
The ability to generate privacy-preserving synthetic versions of sensitive image datasets could unlock numerous ML applications currently constrained by data availability. Due to t…
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Soham De, Leonard Berrada, Jamie Hayes +2
Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training p…
Learning to be adversarially robust and differentially private
Jamie Hayes, Borja Balle, M. Pawan Kumar
We study the difficulties in learning that arise from robust and differentially private optimization. We first study convergence of gradient descent based adversarial training with…
Towards transformation-resilient provenance detection of digital media
Jamie Hayes, Krishnamurthy, Dvijotham +4
Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…
Extensions and limitations of randomized smoothing for robustness guarantees
Jamie Hayes
Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. I…
Unique properties of adversarially trained linear classifiers on Gaussian data
Jamie Hayes
Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research co…
Trade-offs between membership privacy & adversarially robust learning
Jamie Hayes
Historically, machine learning methods have not been designed with security in mind. In turn, this has given rise to adversarial examples, carefully perturbed input samples aimed t…
Evading classifiers in discrete domains with provable optimality guarantees
Bogdan Kulynych, Jamie Hayes, Nikita Samarin +1
Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from h…