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2020 – today
- 2023
[i20]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)- [i19]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) - [i18]Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? CoRR abs/2302.09483 (2023) - [i17]Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini:
Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR abs/2303.03446 (2023) - [i16]Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr:
Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. CoRR abs/2305.05973 (2023) - [i15]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. CoRR abs/2305.08846 (2023) - 2022
- [j1]Xinyu Tang, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, Prateek Mittal:
Machine Learning with Differentially Private Labels: Mechanisms and Frameworks. Proc. Priv. Enhancing Technol. 2022(4): 332-350 (2022) - [c14]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
:
Membership Inference Attacks From First Principles. IEEE Symposium on Security and Privacy 2022: 1897-1914 - [c13]Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal:
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. USENIX Security Symposium 2022: 1433-1450 - [i14]Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu:
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning. CoRR abs/2206.03852 (2022) - [i13]Nicholas Carlini, Vitaly Feldman, Milad Nasr:
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy". CoRR abs/2209.14987 (2022) - [i12]Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini:
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR abs/2210.17546 (2022) - 2021
- [c12]Alireza Bahramali, Milad Nasr, Amir Houmansadr, Dennis Goeckel, Don Towsley:
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems. CCS 2021: 126-140 - [c11]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy 2021: 866-882 - [c10]Milad Nasr, Alireza Bahramali, Amir Houmansadr:
Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations. USENIX Security Symposium 2021: 2705-2722 - [i11]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR abs/2101.04535 (2021) - [i10]Alireza Bahramali, Milad Nasr, Amir Houmansadr, Dennis Goeckel, Don Towsley:
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems. CoRR abs/2102.00918 (2021) - [i9]Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal:
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. CoRR abs/2110.08324 (2021) - [i8]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr:
Membership Inference Attacks From First Principles. CoRR abs/2112.03570 (2021) - 2020
- [c9]Milad Nasr, Michael Carl Tschantz:
Bidding strategies with gender nondiscrimination constraints for online ad auctions. FAT* 2020: 337-347 - [c8]Milad Nasr, Hadi Zolfaghari, Amir Houmansadr, Amirhossein Ghafari:
MassBrowser: Unblocking the Censored Web for the Masses, by the Masses. NDSS 2020 - [i7]Milad Nasr, Alireza Bahramali, Amir Houmansadr:
Blind Adversarial Network Perturbations. CoRR abs/2002.06495 (2020) - [i6]Milad Nasr, Reza Shokri, Amir Houmansadr:
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising. CoRR abs/2007.11524 (2020)
2010 – 2019
- 2019
- [c7]Milad Nasr, Sadegh Farhang, Amir Houmansadr, Jens Grossklags:
Enemy At the Gateways: Censorship-Resilient Proxy Distribution Using Game Theory. NDSS 2019 - [c6]Milad Nasr, Reza Shokri, Amir Houmansadr:
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. IEEE Symposium on Security and Privacy 2019: 739-753 - [i5]Milad Nasr, Michael Carl Tschantz:
Bidding Strategies with Gender Nondiscrimination: Constraints for Online Ad Auctions. CoRR abs/1909.02156 (2019) - 2018
- [c5]Milad Nasr, Reza Shokri, Amir Houmansadr:
Machine Learning with Membership Privacy using Adversarial Regularization. CCS 2018: 634-646 - [c4]Milad Nasr, Alireza Bahramali, Amir Houmansadr:
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning. CCS 2018: 1962-1976 - [i4]Milad Nasr, Reza Shokri, Amir Houmansadr:
Machine Learning with Membership Privacy using Adversarial Regularization. CoRR abs/1807.05852 (2018) - [i3]Milad Nasr, Alireza Bahramali, Amir Houmansadr:
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning. CoRR abs/1808.07285 (2018) - [i2]Milad Nasr, Reza Shokri, Amir Houmansadr:
Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. CoRR abs/1812.00910 (2018) - 2017
- [c3]Milad Nasr, Hadi Zolfaghari, Amir Houmansadr:
The Waterfall of Liberty: Decoy Routing Circumvention that Resists Routing Attacks. CCS 2017: 2037-2052 - [c2]Milad Nasr, Amir Houmansadr, Arya Mazumdar:
Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis. CCS 2017: 2053-2069 - [i1]Milad Nasr, Sadegh Farhang, Amir Houmansadr, Jens Grossklags:
Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution. CoRR abs/1709.04030 (2017) - 2016
- [c1]Milad Nasr, Amir Houmansadr:
GAME OF DECOYS: Optimal Decoy Routing Through Game Theory. CCS 2016: 1727-1738
Coauthor Index
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last updated on 2023-05-18 22:38 CEST by the dblp team
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