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ORCIDNicolas Papernot
- > Home > Persons > Nicolas Papernot
Publications
- 2024
[j9]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
From Differential Privacy to Bounds on Membership Inference: Less can be More. Trans. Mach. Learn. Res. 2024 (2024)- [j8]Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot:
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. Trans. Mach. Learn. Res. 2024 (2024) - [c74]Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR 2024 - [i108]Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR abs/2401.12233 (2024) - [i105]Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot:
Regulation Games for Trustworthy Machine Learning. CoRR abs/2402.03540 (2024) - 2023
- [c66]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P 2023: 175-199 - [c65]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P 2023: 241-257 - [c61]Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS 2023 - [c60]Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS 2023 - [i95]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Is Federated Learning a Practical PET Yet? CoRR abs/2301.04017 (2023) - [i94]Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot:
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. CoRR abs/2302.09183 (2023) - [i93]Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR abs/2303.17046 (2023) - [i91]Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR abs/2305.15594 (2023) - [i87]Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell
, Franziska Boenisch, Nicolas Papernot:
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR abs/2306.08656 (2023) - 2022
- [c47]Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. NeurIPS 2022 - [i78]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
Bounding Membership Inference. CoRR abs/2202.12232 (2022) - [i65]Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. CoRR abs/2209.09024 (2022) - 2021
- [i46]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR abs/2112.02918 (2021)
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