Matthew Jagielski

Nicolas Papernot

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stealing part of a production language model.

[BibT_eX]

[DOI]

Krishnamurthy Dj Dvijotham

Daniel Paleka

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Noise Masking Attacks and Defenses for Pretrained Speech Models.

[BibT_eX]

[DOI]

Om Thakkar

Lun Wang

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models.

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., July, 2023

Scalable Extraction of Training Data from (Production) Language Models.

[BibT_eX]

[DOI]

Eric Wallace

Florian Tramèr

Katherine Lee

CoRR, 2023

Backdoor Attacks for In-Context Learning with Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

Are aligned neural networks adversarially aligned?

[BibT_eX]

[DOI]

CoRR, 2023

A Note On Interpreting Canary Exposure.

[BibT_eX]

[DOI]

CoRR, 2023

Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

Challenges towards the Next Frontier in Privacy.

[BibT_eX]

[DOI]

CoRR, 2023

Students Parrot Their Teachers: Membership Inference on Model Distillation.

[BibT_eX]

[DOI]

Katherine Lee

CoRR, 2023

Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators.

[BibT_eX]

[DOI]

CoRR, 2023

Tight Auditing of Differentially Private Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

Extracting Training Data from Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

SNAP: Efficient Extraction of Private Properties with Poisoning.

[BibT_eX]

[DOI]

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning.

[BibT_eX]

[DOI]

Harsh Chaudhari

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Counterfactual Memorization in Neural Language Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Students Parrot Their Teachers: Membership Inference on Model Distillation.

[BibT_eX]

[DOI]

Katherine Lee

Florian Tramèr

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Are aligned neural networks adversarially aligned?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Privacy Auditing with One (1) Training Run.

[BibT_eX]

[DOI]

Thomas Steinke

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy.

[BibT_eX]

[DOI]

Proceedings of the 16th International Natural Language Generation Conference, 2023

Measuring Forgetting of Memorized Training Examples.

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

Nicolas Papernot

Chiyuan Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Quantifying Memorization Across Neural Language Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning.

[BibT_eX]

[DOI]

Harsh Chaudhari

IACR Cryptol. ePrint Arch., 2022

Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

[BibT_eX]

[DOI]

CoRR, 2022

How to Combine Membership-Inference Attacks on Multiple Updated Models.

[BibT_eX]

[DOI]

CoRR, 2022

Debugging Differential Privacy: A Case Study for Privacy Auditing.

[BibT_eX]

[DOI]

CoRR, 2022

The Privacy Onion Effect: Memorization is Relative.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Subverting Fair Image Search with Generative Adversarial Perturbations.

[BibT_eX]

[DOI]

Avijit Ghosh

Christo Wilson

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Network-Level Adversaries in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 10th IEEE Conference on Communications and Network Security, 2022

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets.

[BibT_eX]

[DOI]

Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

2021

Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) versus QUIC.

[BibT_eX]

[DOI]

J. Cryptol., 2021

Extracting Training Data from Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the 30th USENIX Security Symposium, 2021

Subpopulation Data Poisoning Attacks.

[BibT_eX]

[DOI]

Giorgio Severi

Niklas Pousette Harger

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

2020

High Accuracy and High Fidelity Extraction of Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 29th USENIX Security Symposium, 2020

Auditing Differentially Private Machine Learning: How Private is Private SGD?

[BibT_eX]

[DOI]

Jonathan R. Ullman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Cryptanalytic Extraction of Neural Network Models.

[BibT_eX]

[DOI]