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Jamie Hayes
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Journal Articles
- 2019
- [j3]Jamie Hayes, Luca Melis, George Danezis, Emiliano De Cristofaro:
LOGAN: Membership Inference Attacks Against Generative Models. Proc. Priv. Enhancing Technol. 2019(1): 133-152 (2019) - 2017
- [j2]Giovanni Cherubin, Jamie Hayes, Marc Juarez
:
Website Fingerprinting Defenses at the Application Layer. Proc. Priv. Enhancing Technol. 2017(2): 186-203 (2017) - 2015
- [j1]Jamie Hayes, George Danezis:
Guard Sets for Onion Routing. Proc. Priv. Enhancing Technol. 2015(2): 65-80 (2015)
Conference and Workshop Papers
- 2023
- [c18]Vasilios Mavroudis
, Jamie Hayes:
Adaptive Webpage Fingerprinting from TLS Traces. DSN 2023: 445-458 - [c17]Jamie Hayes, Borja Balle, Saeed Mahloujifar:
Bounding training data reconstruction in DP-SGD. NeurIPS 2023 - [c16]Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou:
Towards Unbounded Machine Unlearning. NeurIPS 2023 - [c15]Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller:
Mnemonist: Locating Model Parameters that Memorize Training Examples. UAI 2023: 1879-1888 - [c14]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium 2023: 1631-1648 - [c13]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. USENIX Security Symposium 2023: 5253-5270 - 2022
- [c12]Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro:
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning. NDSS 2022 - [c11]Borja Balle, Giovanni Cherubin, Jamie Hayes:
Reconstructing Training Data with Informed Adversaries. SP 2022: 1138-1156 - 2020
- [c10]Jamie Hayes:
Extensions and limitations of randomized smoothing for robustness guarantees. CVPR Workshops 2020: 3413-3421 - [c9]Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, J. Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli:
A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. ICLR 2020 - 2018
- [c8]Jamie Hayes:
On Visible Adversarial Perturbations & Digital Watermarking. CVPR Workshops 2018: 1597-1604 - [c7]Jamie Hayes, Olga Ohrimenko
:
Contamination Attacks and Mitigation in Multi-Party Machine Learning. NeurIPS 2018: 6604-6616 - [c6]Jamie Hayes, George Danezis:
Learning Universal Adversarial Perturbations with Generative Models. IEEE Symposium on Security and Privacy Workshops 2018: 43-49 - 2017
- [c5]Jamie Hayes, George Danezis:
Generating steganographic images via adversarial training. NIPS 2017: 1954-1963 - [c4]Ania M. Piotrowska, Jamie Hayes, Tariq Elahi, Sebastian Meiser, George Danezis:
The Loopix Anonymity System. USENIX Security Symposium 2017: 1199-1216 - [c3]Ania M. Piotrowska, Jamie Hayes, Nethanel Gelernter, George Danezis, Amir Herzberg:
AnNotify: A Private Notification Service. WPES@CCS 2017: 5-15 - 2016
- [c2]Jamie Hayes, George Danezis:
k-fingerprinting: A Robust Scalable Website Fingerprinting Technique. USENIX Security Symposium 2016: 1187-1203 - [c1]Jamie Hayes, Carmela Troncoso, George Danezis:
TASP: Towards Anonymity Sets that Persist. WPES@CCS 2016: 177-180
Informal and Other Publications
- 2024
- [i33]Jamie Hayes, Ilia Shumailov, Itay Yona:
Buffer Overflow in Mixture of Experts. CoRR abs/2402.05526 (2024) - [i32]Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, Nicolas Papernot:
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy. CoRR abs/2403.01218 (2024) - [i31]Eleanor Clifford, Adhithya Saravanan, Harry Langford, Cheng Zhang, Yiren Zhao, Robert D. Mullins, Ilia Shumailov, Jamie Hayes:
Locking Machine Learning Models into Hardware. CoRR abs/2405.20990 (2024) - [i30]Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert:
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. CoRR abs/2406.08918 (2024) - [i29]Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon:
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR abs/2406.09073 (2024) - [i28]Hanna Foerster, Robert D. Mullins, Ilia Shumailov, Jamie Hayes:
Beyond Slow Signs in High-fidelity Model Extraction. CoRR abs/2406.10011 (2024) - [i27]Aneesh Pappu, Billy Porter, Ilia Shumailov, Jamie Hayes:
Measuring memorization in RLHF for code completion. CoRR abs/2406.11715 (2024) - [i26]Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan:
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. CoRR abs/2407.00106 (2024) - 2023
- [i25]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. CoRR abs/2301.13188 (2023) - [i24]Jamie Hayes, Saeed Mahloujifar, Borja Balle:
Bounding Training Data Reconstruction in DP-SGD. CoRR abs/2302.07225 (2023) - [i23]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. CoRR abs/2302.07956 (2023) - [i22]Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, Borja Balle:
Differentially Private Diffusion Models Generate Useful Synthetic Images. CoRR abs/2302.13861 (2023) - [i21]Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert:
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. CoRR abs/2307.03928 (2023) - [i20]Leonard Berrada, Soham De, Judy Hanwen Shen, Jamie Hayes, Robert Stanforth, David Stutz, Pushmeet Kohli, Samuel L. Smith, Borja Balle:
Unlocking Accuracy and Fairness in Differentially Private Image Classification. CoRR abs/2308.10888 (2023) - 2022
- [i19]Jamie Hayes, Borja Balle, M. Pawan Kumar:
Learning to be adversarially robust and differentially private. CoRR abs/2201.02265 (2022) - [i18]Borja Balle, Giovanni Cherubin, Jamie Hayes:
Reconstructing Training Data with Informed Adversaries. CoRR abs/2201.04845 (2022) - [i17]Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle:
Unlocking High-Accuracy Differentially Private Image Classification through Scale. CoRR abs/2204.13650 (2022) - 2020
- [i16]Jamie Hayes:
Unique properties of adversarially trained linear classifiers on Gaussian data. CoRR abs/2006.03873 (2020) - [i15]Jamie Hayes:
Extensions and limitations of randomized smoothing for robustness guarantees. CoRR abs/2006.04208 (2020) - [i14]Jamie Hayes:
Provable trade-offs between private & robust machine learning. CoRR abs/2006.04622 (2020) - [i13]Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro:
Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy. CoRR abs/2009.03561 (2020) - [i12]Vasilios Mavroudis, Jamie Hayes:
Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions. CoRR abs/2010.10294 (2020) - [i11]Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande:
Towards transformation-resilient provenance detection of digital media. CoRR abs/2011.07355 (2020) - 2019
- [i10]Jamie Hayes, Olga Ohrimenko:
Contamination Attacks and Mitigation in Multi-Party Machine Learning. CoRR abs/1901.02402 (2019) - 2018
- [i9]Bogdan Kulynych, Jamie Hayes, Nikita Samarin, Carmela Troncoso:
Evading classifiers in discrete domains with provable optimality guarantees. CoRR abs/1810.10939 (2018) - [i8]Jamie Hayes:
A note on hyperparameters in black-box adversarial examples. CoRR abs/1811.06539 (2018) - 2017
- [i7]Jamie Hayes, George Danezis:
ste-GAN-ography: Generating Steganographic Images via Adversarial Training. CoRR abs/1703.00371 (2017) - [i6]Ania M. Piotrowska, Jamie Hayes, Tariq Elahi, Sebastian Meiser, George Danezis:
The Loopix Anonymity System. CoRR abs/1703.00536 (2017) - [i5]Jamie Hayes, Luca Melis, George Danezis, Emiliano De Cristofaro:
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks. CoRR abs/1705.07663 (2017) - [i4]Jamie Hayes, George Danezis:
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples. CoRR abs/1708.05207 (2017) - 2016
- [i3]Jamie Hayes:
Traffic Confirmation Attacks Despite Noise. CoRR abs/1601.04893 (2016) - [i2]Ania M. Piotrowska, Jamie Hayes, Nethanel Gelernter, George Danezis, Amir Herzberg:
AnoNotify: A Private Notification Service. IACR Cryptol. ePrint Arch. 2016: 466 (2016) - 2015
- [i1]Jamie Hayes, George Danezis:
Better open-world website fingerprinting. CoRR abs/1509.00789 (2015)
Coauthor Index
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