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ORCIDSaeed Mahloujifar
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2020 – today
- 2024
[c37]Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal:
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization. ICML 2024- [c36]Parham Yassini, Khaled M. Diab, Saeed Mahloujifar, Mohamed Hefeeda:
Horus: Granular In-Network Task Scheduler for Cloud Datacenters. NSDI 2024: 1-22 - [i43]Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal:
Private Fine-tuning of Large Language Models with Zeroth-order Optimization. CoRR abs/2401.04343 (2024) - [i42]Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo:
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR abs/2403.05598 (2024) - [i41]Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert:
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR abs/2404.02866 (2024) - 2023
- [c35]Sanjam Garg
, Aarushi Goel, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Guru-Vamsi Policharla, Mingyuan Wang:
Experimenting with Zero-Knowledge Proofs of Training. CCS 2023: 1880-1894 - [c34]Xiangyu Qi, Tinghao Xie, Yiming Li, Saeed Mahloujifar, Prateek Mittal:
Revisiting the Assumption of Latent Separability for Backdoor Defenses. ICLR 2023 - [c33]Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. ICML 2023: 6760-6785 - [c32]Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr:
Effectively Using Public Data in Privacy Preserving Machine Learning. ICML 2023: 25718-25732 - [c31]Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Uncovering Adversarial Risks of Test-Time Adaptation. ICML 2023: 37456-37495 - [c30]Jamie Hayes, Borja Balle, Saeed Mahloujifar:
Bounding training data reconstruction in DP-SGD. NeurIPS 2023 - [c29]Jiachen T. Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal:
A Randomized Approach to Tight Privacy Accounting. NeurIPS 2023 - [c28]Chong Xiang, Alexander Valtchanov, Saeed Mahloujifar, Prateek Mittal:
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking. SP 2023: 1329-1347 - [c27]Xiangyu Qi, Tinghao Xie, Jiachen T. Wang, Tong Wu, Saeed Mahloujifar, Prateek Mittal:
Towards A Proactive ML Approach for Detecting Backdoor Poison Samples. USENIX Security Symposium 2023: 1685-1702 - [i40]Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Uncovering Adversarial Risks of Test-Time Adaptation. CoRR abs/2301.12576 (2023) - [i39]Jamie Hayes, Saeed Mahloujifar, Borja Balle:
Bounding Training Data Reconstruction in DP-SGD. CoRR abs/2302.07225 (2023) - [i38]Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. CoRR abs/2302.10980 (2023) - [i37]Jiachen T. Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal:
A Randomized Approach for Tight Privacy Accounting. CoRR abs/2304.07927 (2023) - [i36]Jaiden Fairoze, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang:
Publicly Detectable Watermarking for Language Models. CoRR abs/2310.18491 (2023) - [i35]Sanjam Garg, Aarushi Goel, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Guru-Vamsi Policharla, Mingyuan Wang:
Experimenting with Zero-Knowledge Proofs of Training. IACR Cryptol. ePrint Arch. 2023: 1345 (2023) - [i34]Jaiden Fairoze, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang:
Publicly Detectable Watermarking for Language Models. IACR Cryptol. ePrint Arch. 2023: 1661 (2023) - 2022
- [j2]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) - [c26]Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal:
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. AISTATS 2022: 7587-7624 - [c25]Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. AISec@CCS 2022: 91-102 - [c24]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? ICLR 2022 - [c23]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. NeurIPS 2022 - [c22]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang:
Overparameterization from Computational Constraints. NeurIPS 2022 - [c21]Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang, Ruoxi Jia, Prateek Mittal:
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning. NeurIPS 2022 - [c20]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Parameterizing Activation Functions for Adversarial Robustness. SP (Workshops) 2022: 80-87 - [c19]Saeed Mahloujifar, Esha Ghosh, Melissa Chase:
Property Inference from Poisoning. SP 2022: 1120-1137 - [c18]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 - [c17]Chong Xiang, Saeed Mahloujifar, Prateek Mittal:
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier. USENIX Security Symposium 2022: 2065-2082 - [i33]Chong Xiang, Alexander Valtchanov, Saeed Mahloujifar, Prateek Mittal:
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking. CoRR abs/2202.01811 (2022) - [i32]Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode, Somesh Jha:
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms. CoRR abs/2204.06106 (2022) - [i31]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. CoRR abs/2204.13779 (2022) - [i30]Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, Prateek Mittal:
Circumventing Backdoor Defenses That Are Based on Latent Separability. CoRR abs/2205.13613 (2022) - [i29]Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, Prateek Mittal:
Fight Poison with Poison: Detecting Backdoor Poison Samples via Decoupling Benign Correlations. CoRR abs/2205.13616 (2022) - [i28]Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. CoRR abs/2207.10825 (2022) - [i27]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang:
Overparameterized (robust) models from computational constraints. CoRR abs/2208.12926 (2022) - [i26]Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang, Ruoxi Jia, Prateek Mittal:
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning. CoRR abs/2209.07716 (2022) - [i25]Ashwinee Panda, Xinyu Tang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning. CoRR abs/2212.04486 (2022) - 2021
- [c16]Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian:
Model-Targeted Poisoning Attacks with Provable Convergence. ICML 2021: 10000-10010 - [c15]Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta:
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks. NeurIPS 2021: 10862-10875 - [c14]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr:
Is Private Learning Possible with Instance Encoding? SP 2021: 410-427 - [c13]Omid Etesami, Ji Gao, Saeed Mahloujifar, Mohammad Mahmoody:
Polynomial-Time Targeted Attacks on Coin Tossing for Any Number of Corruptions. TCC (2) 2021: 718-750 - [i24]Melissa Chase, Esha Ghosh, Saeed Mahloujifar:
Property Inference From Poisoning. CoRR abs/2101.11073 (2021) - [i23]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Improving Adversarial Robustness Using Proxy Distributions. CoRR abs/2104.09425 (2021) - [i22]Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa:
Membership Inference on Word Embedding and Beyond. CoRR abs/2106.11384 (2021) - [i21]Nicholas Carlini, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Florian Tramèr:
NeuraCrypt is not private. CoRR abs/2108.07256 (2021) - [i20]Chong Xiang, Saeed Mahloujifar, Prateek Mittal:
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier. CoRR abs/2108.09135 (2021) - [i19]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Parameterizing Activation Functions for Adversarial Robustness. CoRR abs/2110.05626 (2021) - [i18]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) - [i17]Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal:
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. CoRR abs/2112.06274 (2021) - [i16]Melissa Chase, Esha Ghosh, Saeed Mahloujifar:
Property Inference from Poisoning. IACR Cryptol. ePrint Arch. 2021: 99 (2021) - [i15]Omid Etesami, Ji Gao, Saeed Mahloujifar, Mohammad Mahmoody:
Polynomial-time targeted attacks on coin tossing for any number of corruptions. IACR Cryptol. ePrint Arch. 2021: 1464 (2021) - 2020
- [j1]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
Learning under p-tampering poisoning attacks. Ann. Math. Artif. Intell. 88(7): 759-792 (2020) - [c12]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarially Robust Learning Could Leverage Computational Hardness. ALT 2020: 364-385 - [c11]Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Lower Bounds for Adversarially Robust PAC Learning under Evasion and Hybrid Attacks. ICMLA 2020: 717-722 - [c10]Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Lower Bounds for Adversarially Robust PAC Learning. ISAIM 2020 - [c9]Omid Etesami, Saeed Mahloujifar, Mohammad Mahmoody:
Computational Concentration of Measure: Optimal Bounds, Reductions, and More. SODA 2020: 345-363 - [i14]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta:
Obliviousness Makes Poisoning Adversaries Weaker. CoRR abs/2003.12020 (2020) - [i13]Fnu Suya, Saeed Mahloujifar, David Evans, Yuan Tian:
Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds. CoRR abs/2006.16469 (2020) - [i12]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr:
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR abs/2011.05315 (2020)
2010 – 2019
- 2019
- [c8]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure. AAAI 2019: 4536-4543 - [c7]Saeed Mahloujifar, Mohammad Mahmoody:
Can Adversarially Robust Learning LeverageComputational Hardness? ALT 2019: 581-609 - [c6]Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed:
Data Poisoning Attacks in Multi-Party Learning. ICML 2019: 4274-4283 - [c5]Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans:
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. NeurIPS 2019: 5210-5221 - [i11]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarially Robust Learning Could Leverage Computational Hardness. CoRR abs/1905.11564 (2019) - [i10]Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans:
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. CoRR abs/1905.12202 (2019) - [i9]Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Lower Bounds for Adversarially Robust PAC Learning. CoRR abs/1906.05815 (2019) - [i8]Omid Etesami, Saeed Mahloujifar, Mohammad Mahmoody:
Computational Concentration of Measure: Optimal Bounds, Reductions, and More. CoRR abs/1907.05401 (2019) - 2018
- [c4]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
Learning under $p$-Tampering Attacks. ALT 2018: 572-596 - [c3]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
Learning under p-Tampering Attacks. ISAIM 2018 - [c2]Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution. NeurIPS 2018: 10380-10389 - [i7]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure. CoRR abs/1809.03063 (2018) - [i6]Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed:
Multi-party Poisoning through Generalized p-Tampering. CoRR abs/1809.03474 (2018) - [i5]Saeed Mahloujifar, Mohammad Mahmoody:
Can Adversarially Robust Learning Leverage Computational Hardness? CoRR abs/1810.01407 (2018) - [i4]Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution. CoRR abs/1810.12272 (2018) - [i3]Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed:
Multi-party Poisoning through Generalized p-Tampering. IACR Cryptol. ePrint Arch. 2018: 854 (2018) - 2017
- [c1]Saeed Mahloujifar, Mohammad Mahmoody:
Blockwise p-Tampering Attacks on Cryptographic Primitives, Extractors, and Learners. TCC (2) 2017: 245-279 - [i2]Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody:
Learning under p-Tampering Attacks. CoRR abs/1711.03707 (2017) - [i1]Saeed Mahloujifar, Mohammad Mahmoody:
Blockwise p-Tampering Attacks on Cryptographic Primitives, Extractors, and Learners. IACR Cryptol. ePrint Arch. 2017: 950 (2017)
Coauthor Index
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