Jamie Hayes | DeepAI

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∙ 08/21/2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Privacy-preserving machine learning aims to train models on private data...

0 Leonard Berrada, et al. ∙

∙ 07/08/2023

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy

We explore Reconstruction Robustness (ReRo), which was recently proposed...

0 Georgios Kaissis, et al. ∙

∙ 02/27/2023

Differentially Private Diffusion Models Generate Useful Synthetic Images

The ability to generate privacy-preserving synthetic versions of sensiti...

3 Sahra Ghalebikesabi, et al. ∙

∙ 02/15/2023

Tight Auditing of Differentially Private Machine Learning

Auditing mechanisms for differential privacy use probabilistic means to ...

0 Milad Nasr, et al. ∙

∙ 01/30/2023

Extracting Training Data from Diffusion Models

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...

0 Nicholas Carlini, et al. ∙

∙ 04/28/2022

Unlocking High-Accuracy Differentially Private Image Classification through Scale

Differential Privacy (DP) provides a formal privacy guarantee preventing...

0 Soham De, et al. ∙

∙ 01/13/2022

Reconstructing Training Data with Informed Adversaries

Given access to a machine learning model, can an adversary reconstruct t...

11 Borja Balle, et al. ∙

∙ 01/06/2022

Learning to be adversarially robust and differentially private

We study the difficulties in learning that arise from robust and differe...

0 Jamie Hayes, et al. ∙

∙ 11/14/2020

Towards transformation-resilient provenance detection of digital media

Advancements in deep generative models have made it possible to synthesi...

7 Jamie Hayes, et al. ∙

∙ 10/19/2020

Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions

The widespread adoption of encrypted communications (e.g., the TLS proto...

0 Vasilios Mavroudis, et al. ∙

∙ 09/08/2020

Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy

Federated Learning (FL) allows multiple participants to collaboratively ...

0 Mohammad Naseri, et al. ∙

∙ 06/08/2020

Provable trade-offs between private robust machine learning

Historically, machine learning methods have not been designed with secur...

0 Jamie Hayes, et al. ∙

∙ 06/07/2020

Extensions and limitations of randomized smoothing for robustness guarantees

Randomized smoothing, a method to certify a classifier's decision on an ...

0 Jamie Hayes, et al. ∙

∙ 06/06/2020

Unique properties of adversarially trained linear classifiers on Gaussian data

Machine learning models are vulnerable to adversarial perturbations, tha...

0 Jamie Hayes, et al. ∙

∙ 01/08/2019

Contamination Attacks and Mitigation in Multi-Party Machine Learning

Machine learning is data hungry; the more data a model has access to in ...

0 Jamie Hayes, et al. ∙

∙ 11/15/2018

A note on hyperparameters in black-box adversarial examples

Since Biggio et al. (2013) and Szegedy et al. (2013) first drew attentio...

0 Jamie Hayes, et al. ∙

∙ 10/25/2018

Evading classifiers in discrete domains with provable optimality guarantees

Security-critical applications such as malware, fraud, or spam detection...

0 Bogdan Kulynych, et al. ∙

∙ 08/17/2017

Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples

Neural networks are known to be vulnerable to adversarial examples, inpu...

0 Jamie Hayes, et al. ∙

∙ 03/01/2017

Generating Steganographic Images via Adversarial Training

Adversarial training was recently shown to be competitive against superv...

0 Jamie Hayes, et al. ∙

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