Jamie Hayes

According to our database1, Jamie Hayes authored at least 48 papers between 2015 and 2024.

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Bibliography

2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD.
CoRR, 2024

UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI.
CoRR, 2024

Measuring memorization in RLHF for code completion.
CoRR, 2024

Beyond Slow Signs in High-fidelity Model Extraction.
CoRR, 2024

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.
CoRR, 2024

Locking Machine Learning Models into Hardware.
CoRR, 2024

Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy.
CoRR, 2024

Buffer Overflow in Mixture of Experts.
CoRR, 2024

Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification.
CoRR, 2023

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.
CoRR, 2023

Differentially Private Diffusion Models Generate Useful Synthetic Images.
CoRR, 2023

Tight Auditing of Differentially Private Machine Learning.
Proceedings of the 32nd USENIX Security Symposium, 2023

Extracting Training Data from Diffusion Models.
Proceedings of the 32nd USENIX Security Symposium, 2023

Mnemonist: Locating Model Parameters that Memorize Training Examples.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Towards Unbounded Machine Unlearning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Bounding training data reconstruction in DP-SGD.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptive Webpage Fingerprinting from TLS Traces.
Proceedings of the 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Network, 2023

2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale.
CoRR, 2022

Learning to be adversarially robust and differentially private.
CoRR, 2022

Reconstructing Training Data with Informed Adversaries.
Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.
Proceedings of the 29th Annual Network and Distributed System Security Symposium, 2022

2020
Towards transformation-resilient provenance detection of digital media.
CoRR, 2020

Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions.
CoRR, 2020

Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy.
CoRR, 2020

Provable trade-offs between private & robust machine learning.
CoRR, 2020

Unique properties of adversarially trained linear classifiers on Gaussian data.
CoRR, 2020

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.
Proceedings of the 8th International Conference on Learning Representations, 2020

Extensions and limitations of randomized smoothing for robustness guarantees.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
LOGAN: Membership Inference Attacks Against Generative Models.
Proc. Priv. Enhancing Technol., 2019

2018
A note on hyperparameters in black-box adversarial examples.
CoRR, 2018

Evading classifiers in discrete domains with provable optimality guarantees.
CoRR, 2018

Learning Universal Adversarial Perturbations with Generative Models.
Proceedings of the 2018 IEEE Security and Privacy Workshops, 2018

Contamination Attacks and Mitigation in Multi-Party Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Visible Adversarial Perturbations & Digital Watermarking.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017
Website Fingerprinting Defenses at the Application Layer.
Proc. Priv. Enhancing Technol., 2017

Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples.
CoRR, 2017

LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks.
CoRR, 2017

ste-GAN-ography: Generating Steganographic Images via Adversarial Training.
CoRR, 2017

AnNotify: A Private Notification Service.
Proceedings of the 2017 on Workshop on Privacy in the Electronic Society, Dallas, TX, USA, October 30, 2017

The Loopix Anonymity System.
Proceedings of the 26th USENIX Security Symposium, 2017

Generating steganographic images via adversarial training.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
AnoNotify: A Private Notification Service.
IACR Cryptol. ePrint Arch., 2016

Traffic Confirmation Attacks Despite Noise.
CoRR, 2016

TASP: Towards Anonymity Sets that Persist.
Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society, 2016

k-fingerprinting: A Robust Scalable Website Fingerprinting Technique.
Proceedings of the 25th USENIX Security Symposium, 2016

2015
Guard Sets for Onion Routing.
Proc. Priv. Enhancing Technol., 2015

Better open-world website fingerprinting.
CoRR, 2015


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