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2020 – today
- 2023
[c11]Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr:
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. ICCV 2023: 4707-4717- [c10]Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar:
On the Pitfalls of Security Evaluation of Robust Federated Learning. SP (Workshops) 2023: 57-68 - [c9]Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr:
Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. USENIX Security Symposium 2023: 1721-1738 - 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) - [c8]Vasisht Duddu
, Antoine Boutet, Virat Shejwalkar:
Towards privacy aware deep learning for embedded systems. SAC 2022: 520-529 - [c7]Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar:
Security Analysis of SplitFed Learning. SenSys 2022: 987-993 - [c6]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. SP 2022: 1354-1371 - [c5]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 - [i11]Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta:
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR abs/2210.01864 (2022) - [i10]Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr:
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. CoRR abs/2211.00453 (2022) - [i9]Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar:
Security Analysis of SplitFed Learning. CoRR abs/2212.01716 (2022) - 2021
- [c4]Virat Shejwalkar, Amir Houmansadr:
Membership Privacy for Machine Learning Models Through Knowledge Transfer. AAAI 2021: 9549-9557 - [c3]Virat Shejwalkar, Amir Houmansadr:
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. NDSS 2021 - [i8]Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage:
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR abs/2108.10241 (2021) - [i7]Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr:
FSL: Federated Supermask Learning. CoRR abs/2110.04350 (2021) - [i6]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) - 2020
- [j1]Nazanin Takbiri, Virat Shejwalkar, Amir Houmansadr, Dennis L. Goeckel, Hossein Pishro-Nik:
Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms. IEEE Wirel. Commun. Lett. 9(11): 2005-2009 (2020) - [c2]Vasisht Duddu, Antoine Boutet, Virat Shejwalkar:
Quantifying Privacy Leakage in Graph Embedding. MobiQuitous 2020: 76-85 - [i5]Vasisht Duddu, Antoine Boutet, Virat Shejwalkar:
Quantifying Privacy Leakage in Graph Embedding. CoRR abs/2010.00906 (2020) - [i4]Vasisht Duddu, Antoine Boutet, Virat Shejwalkar:
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning. CoRR abs/2010.00912 (2020)
2010 – 2019
- 2019
- [c1]Virat Shejwalkar, Amir Houmansadr, Hossein Pishro-Nik, Dennis Goeckel:
Revisiting utility metrics for location privacy-preserving mechanisms. ACSAC 2019: 313-327 - [i3]Virat Shejwalkar, Amir Houmansadr:
Reconciling Utility and Membership Privacy via Knowledge Distillation. CoRR abs/1906.06589 (2019) - [i2]Nazanin Takbiri, Virat Shejwalkar, Amir Houmansadr, Dennis L. Goeckel, Hossein Pishro-Nik:
Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms. CoRR abs/1912.02209 (2019) - [i1]Hongyan Chang, Virat Shejwalkar, Reza Shokri, Amir Houmansadr:
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. CoRR abs/1912.11279 (2019)
Coauthor Index
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