Milad Nasr
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
29
Venues
7
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
29 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | CCS | Cascading Adversarial Bias from Injection to Distillation in Language Models. | Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov, Milad Nasr, Alina Oprea |
| 2025 | CCS | Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks. | Milad Nasr, Yanick Fratantonio, Luca Invernizzi, Ange Albertini, Loua Farah, Alex Petit-Bianco, Andreas Terzis, Kurt Thomas, Elie Bursztein, Nicholas Carlini |
| 2025 | ICLR | Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. | Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang |
| 2025 | ICLR | The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. | Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis |
| 2025 | ICLR | Scalable Extraction of Training Data from Aligned, Production Language Models. | Milad Nasr, Javier Rando, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramr, Katherine Lee |
| 2025 | ICLR | Privacy Auditing of Large Language Models. | Ashwinee Panda, Xinyu Tang, Christopher A. Choquette-Choo, Milad Nasr, Prateek Mittal |
| 2025 | ICLR | On Evaluating the Durability of Safeguards for Open-Weight LLMs. | Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson |
| 2025 | ICML | AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses. | Nicholas Carlini, Edoardo Debenedetti, Javier Rando, Milad Nasr, Florian Tramr |
| 2025 | ICML | Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. | Yangsibo Huang, Milad Nasr, Anastasios Nikolas Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Liu, Ion Stoica, Florian Tramr, Chiyuan Zhang |
| 2025 | NAACL | Measuring memorization in language models via probabilistic extraction. | Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, A. Feder Cooper |
| 2025 | SP | SoK: Watermarking for AI-Generated Content. | Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andrs Fbrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramr, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song |
| 2024 | ICML | Stealing part of a production language model. | Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramr |
| 2024 | ICML | Auditing Private Prediction. | Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr |
| 2024 | NAACL | Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. | Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr |
| 2023 | ICML | Why Is Public Pretraining Necessary for Private Model Training? | Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang |
| 2023 | ICML | Effectively Using Public Data in Privacy Preserving Machine Learning. | Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr |
| 2023 | INLG | Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. | Daphne Ippolito, Nicholas Carlini, Katherine Lee, Milad Nasr, Yun William Yu |
| 2023 | INLG | Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. | Daphne Ippolito, Florian Tramr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini |
| 2022 | SP | Membership Inference Attacks From First Principles. | Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramr |
| 2021 | CCS | Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems. | Alireza Bahramali, Milad Nasr, Amir Houmansadr, Dennis Goeckel, Don Towsley |
| 2021 | SP | Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. | Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini |
| 2020 | NDSS | MassBrowser: Unblocking the Censored Web for the Masses, by the Masses. | Milad Nasr, Hadi Zolfaghari, Amir Houmansadr, Amirhossein Ghafari |
| 2019 | NDSS | Enemy At the Gateways: Censorship-Resilient Proxy Distribution Using Game Theory. | Milad Nasr, Sadegh Farhang, Amir Houmansadr, Jens Grossklags |
| 2019 | SP | Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. | Milad Nasr, Reza Shokri, Amir Houmansadr |
| 2018 | CCS | DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning. | Milad Nasr, Alireza Bahramali, Amir Houmansadr |
| 2018 | CCS | Machine Learning with Membership Privacy using Adversarial Regularization. | Milad Nasr, Reza Shokri, Amir Houmansadr |
| 2017 | CCS | Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis. | Milad Nasr, Amir Houmansadr, Arya Mazumdar |
| 2017 | CCS | The Waterfall of Liberty: Decoy Routing Circumvention that Resists Routing Attacks. | Milad Nasr, Hadi Zolfaghari, Amir Houmansadr |
| 2016 | CCS | GAME OF DECOYS: Optimal Decoy Routing Through Game Theory. | Milad Nasr, Amir Houmansadr |