Milad Nasr - RankMe

91 papers A* 34B 2Journal 52Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs. CoRR Chuan Guo, Juan Felipe Ceron Uribe, Sicheng Zhu, Christopher A. Choquette-Choo, Stephanie Lin, Nikhil Kandpal, Milad Nasr, Rai, Sam Toyer, Miles Wang, Yaodong Yu, Alex Beutel, Kai Xiao
2026	J	`jnl`	Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models. CoRR Harsh Chaudhari, Ethan Rathbun, Hanna Foerster, Jamie Hayes, Matthew Jagielski, Milad Nasr, Ilia Shumailov, Alina Oprea
2025	A*	`conf`	AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses. ICML Nicholas Carlini, Edoardo Debenedetti, Javier Rando, Milad Nasr, Florian Tramèr
2025	J	`jnl`	AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses. CoRR Nicholas Carlini, Javier Rando, Edoardo Debenedetti, Milad Nasr, Florian Tramèr
2025	A*	`conf`	Cascading Adversarial Bias from Injection to Distillation in Language Models. CCS Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov, Milad Nasr, Alina Oprea
2025	J	`jnl`	Cascading Adversarial Bias from Injection to Distillation in Language Models. CoRR Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov, Milad Nasr, Alina Oprea
2025	A*	`conf`	Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks. CCS Milad Nasr, Yanick Fratantonio, Luca Invernizzi, Ange Albertini, Loua Farah, Alex Petit-Bianco, Andreas Terzis, Kurt Thomas, Elie Bursztein, Nicholas Carlini
2025	J	`jnl`	Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks. CoRR Milad Nasr, Yanick Fratantonio, Luca Invernizzi, Ange Albertini, Loua Farah, Alex Petit-Bianco, Andreas Terzis, Kurt Thomas, Elie Bursztein, Nicholas Carlini
2025	A*	`conf`	Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. ICML 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 Tramèr, Chiyuan Zhang
2025	J	`jnl`	Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. CoRR Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Ziyu Liu, Ion Stoica, Florian Tramèr, Chiyuan Zhang
2025	J	`jnl`	Hush! Protecting Secrets During Model Training: An Indistinguishability Approach. CoRR Arun Ganesh, Brendan McMahan, Milad Nasr, Thomas Steinke, Abhradeep Thakurta
2025	J	`jnl`	LLMs unlock new paths to monetizing exploits. CoRR Nicholas Carlini, Milad Nasr, Edoardo Debenedetti, Barry Wang, Christopher A. Choquette-Choo, Daphne Ippolito, Florian Tramèr, Matthew Jagielski
2025	J	`jnl`	Lessons from Defending Gemini Against Indirect Prompt Injections. CoRR Chongyang Shi, Sharon Lin, Shuang Song, Jamie Hayes, Ilia Shumailov, Itay Yona, Juliette Pluto, Aneesh Pappu, Christopher A. Choquette-Choo, Milad Nasr, Chawin Sitawarin, Gena Gibson, Andreas Terzis, John Flynn
2025	—	`conf`	Measuring memorization in language models via probabilistic extraction. NAACL (Long Papers) Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, A. Feder Cooper
2025	A*	`conf`	On Evaluating the Durability of Safeguards for Open-Weight LLMs. ICLR Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson
2025	A*	`conf`	Privacy Auditing of Large Language Models. ICLR Ashwinee Panda, Xinyu Tang, Christopher A. Choquette-Choo, Milad Nasr, Prateek Mittal
2025	J	`jnl`	Privacy Auditing of Large Language Models. CoRR Ashwinee Panda, Xinyu Tang, Milad Nasr, Christopher A. Choquette-Choo, Prateek Mittal
2025	J	`jnl`	Private Fine-tuning of Large Language Models with Zeroth-order Optimization. Trans. Mach. Learn. Res. Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal
2025	J	`jnl`	Rebellion: Noise-Robust Reasoning Training for Audio Reasoning Models. CoRR Tiansheng Huang, Virat Shejwalkar, Oscar Chang, Milad Nasr, Ling Liu
2025	A*	`conf`	Scalable Extraction of Training Data from Aligned, Production Language Models. ICLR Milad Nasr, Javier Rando, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramèr, Katherine Lee
2025	A*	`conf`	SoK: Watermarking for AI-Generated Content. SP Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andrés Fábrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramèr, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song
2025	J	`jnl`	Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. CoRR Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper
2025	J	`jnl`	The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections. CoRR Milad Nasr, Nicholas Carlini, Chawin Sitawarin, Sander V. Schulhoff, Jamie Hayes, Michael Ilie, Juliette Pluto, Shuang Song, Harsh Chaudhari, Ilia Shumailov, Abhradeep Thakurta, Kai Yuanqing Xiao, Andreas Terzis, Florian Tramèr
2025	A*	`conf`	The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. ICLR Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis
2025	A*	`conf`	Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. ICLR Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang
2024	A*	`conf`	Auditing Private Prediction. ICML Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
2024	J	`jnl`	Auditing Private Prediction. CoRR Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
2024	J	`jnl`	Avoiding Generative Model Writer's Block With Embedding Nudging. CoRR Ali Zand, Milad Nasr
2024	J	`jnl`	On Evaluating the Durability of Safeguards for Open-Weight LLMs. CoRR Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson
2024	J	`jnl`	Phantom: General Trigger Attacks on Retrieval Augmented Language Generation. CoRR Harsh Chaudhari, Giorgio Severi, John Abascal, Matthew Jagielski, Christopher A. Choquette-Choo, Milad Nasr, Cristina Nita-Rotaru, Alina Oprea
2024	A*	`conf`	Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
2024	J	`jnl`	Private Fine-tuning of Large Language Models with Zeroth-order Optimization. CoRR Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal
2024	A*	`conf`	Query-Based Adversarial Prompt Generation. NeurIPS Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr
2024	J	`jnl`	Query-Based Adversarial Prompt Generation. CoRR Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr
2024	J	`jnl`	Remote Timing Attacks on Efficient Language Model Inference. CoRR Nicholas Carlini, Milad Nasr
2024	J	`jnl`	SoK: Watermarking for AI-Generated Content. CoRR Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andrés Fábrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramèr, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song
2024	J	`jnl`	Stealing Part of a Production Language Model. CoRR 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 Tramèr
2024	A*	`conf`	Stealing part of a production language model. ICML 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 Tramèr
2024	—	`conf`	Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. NAACL-HLT Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr
2024	J	`jnl`	The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. CoRR Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis
2024	J	`jnl`	Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. CoRR Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang
2023	A*	`conf`	Are aligned neural networks adversarially aligned? NeurIPS Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt
2023	J	`jnl`	Are aligned neural networks adversarially aligned? CoRR Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt
2023	A*	`conf`	Effectively Using Public Data in Privacy Preserving Machine Learning. ICML Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr
2023	A*	`conf`	Extracting Training Data from Diffusion Models. USENIX Security Symposium Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
2023	J	`jnl`	Extracting Training Data from Diffusion Models. CoRR Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
2023	J	`jnl`	Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems. IEEE Data Eng. Bull. Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu
2023	B	`conf`	Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. INLG Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
2023	A*	`conf`	Privacy Auditing with One (1) Training Run. NeurIPS Thomas Steinke, Milad Nasr, Matthew Jagielski
2023	J	`jnl`	Privacy Auditing with One (1) Training Run. CoRR Thomas Steinke, Milad Nasr, Matthew Jagielski
2023	J	`jnl`	Privacy Side Channels in Machine Learning Systems. CoRR Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr
2023	J	`jnl`	Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. CoRR Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr
2023	J	`jnl`	Report of the 1st Workshop on Generative AI and Law. CoRR A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark A. Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide
2023	B	`conf`	Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. INLG Daphne Ippolito, Nicholas Carlini, Katherine Lee, Milad Nasr, Yun William Yu
2023	J	`jnl`	Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. CoRR Daphne Ippolito, Nicholas Carlini, Katherine Lee, Milad Nasr, Yun William Yu
2023	J	`jnl`	Scalable Extraction of Training Data from (Production) Language Models. CoRR Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
2023	A*	`conf`	Students Parrot Their Teachers: Membership Inference on Model Distillation. NeurIPS Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramèr
2023	J	`jnl`	Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini
2023	A*	`conf`	Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
2023	J	`jnl`	Tight Auditing of Differentially Private Machine Learning. CoRR Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
2023	A*	`conf`	Why Is Public Pretraining Necessary for Private Model Training? ICML Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang
2023	J	`jnl`	Why Is Public Pretraining Necessary for Private Model Training? CoRR Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
2022	J	`jnl`	FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning. CoRR Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu
2022	J	`jnl`	Machine Learning with Differentially Private Labels: Mechanisms and Frameworks. Proc. Priv. Enhancing Technol. Xinyu Tang, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, Prateek Mittal
2022	A*	`conf`	Membership Inference Attacks From First Principles. SP Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
2022	A*	`conf`	Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. USENIX Security Symposium Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal
2022	J	`jnl`	No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy". CoRR Nicholas Carlini, Vitaly Feldman, Milad Nasr
2022	J	`jnl`	Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
2021	A*	`conf`	Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. SP Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
2021	J	`jnl`	Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
2021	A*	`conf`	Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations. USENIX Security Symposium Milad Nasr, Alireza Bahramali, Amir Houmansadr
2021	J	`jnl`	Membership Inference Attacks From First Principles. CoRR Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
2021	J	`jnl`	Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. CoRR Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal
2021	A*	`conf`	Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems. CCS Alireza Bahramali, Milad Nasr, Amir Houmansadr, Dennis Goeckel, Don Towsley
2021	J	`jnl`	Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems. CoRR Alireza Bahramali, Milad Nasr, Amir Houmansadr, Dennis Goeckel, Don Towsley
2020	—	`conf`	Bidding strategies with gender nondiscrimination constraints for online ad auctions. FAT* Milad Nasr, Michael Carl Tschantz
2020	J	`jnl`	Blind Adversarial Network Perturbations. CoRR Milad Nasr, Alireza Bahramali, Amir Houmansadr
2020	J	`jnl`	Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising. CoRR Milad Nasr, Reza Shokri, Amir Houmansadr
2020	A*	`conf`	MassBrowser: Unblocking the Censored Web for the Masses, by the Masses. NDSS Milad Nasr, Hadi Zolfaghari, Amir Houmansadr, Amirhossein Ghafari
2019	J	`jnl`	Bidding Strategies with Gender Nondiscrimination: Constraints for Online Ad Auctions. CoRR Milad Nasr, Michael Carl Tschantz
2019	A*	`conf`	Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. IEEE Symposium on Security and Privacy Milad Nasr, Reza Shokri, Amir Houmansadr
2019	A*	`conf`	Enemy At the Gateways: Censorship-Resilient Proxy Distribution Using Game Theory. NDSS Milad Nasr, Sadegh Farhang, Amir Houmansadr, Jens Grossklags
2018	J	`jnl`	Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. CoRR Milad Nasr, Reza Shokri, Amir Houmansadr
2018	A*	`conf`	DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning. CCS Milad Nasr, Alireza Bahramali, Amir Houmansadr
2018	J	`jnl`	DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning. CoRR Milad Nasr, Alireza Bahramali, Amir Houmansadr
2018	A*	`conf`	Machine Learning with Membership Privacy using Adversarial Regularization. CCS Milad Nasr, Reza Shokri, Amir Houmansadr
2018	J	`jnl`	Machine Learning with Membership Privacy using Adversarial Regularization. CoRR Milad Nasr, Reza Shokri, Amir Houmansadr
2017	A*	`conf`	Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis. CCS Milad Nasr, Amir Houmansadr, Arya Mazumdar
2017	J	`jnl`	Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution. CoRR Milad Nasr, Sadegh Farhang, Amir Houmansadr, Jens Grossklags
2017	A*	`conf`	The Waterfall of Liberty: Decoy Routing Circumvention that Resists Routing Attacks. CCS Milad Nasr, Hadi Zolfaghari, Amir Houmansadr
2016	A*	`conf`	GAME OF DECOYS: Optimal Decoy Routing Through Game Theory. CCS Milad Nasr, Amir Houmansadr