Virat Shejwalkar

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 24

Top Topics

Machine Learning

Location Privacy

Top Venues

USENIX Security Symposium

Publications

Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr
Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. USENIX Security Symposium (2023)
Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. ICCV (2023)
Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar
On the Pitfalls of Security Evaluation of Robust Federated Learning. SP Workshops (2023)
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR (2022)
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) (2022)
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Towards privacy aware deep learning for embedded systems. SAC (2022)
Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. CoRR (2022)
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)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. IEEE Symposium on Security and Privacy (2022)
Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar
Security Analysis of SplitFed Learning. SenSys (2022)
Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima Muhammad Anwar
Security Analysis of SplitFed Learning. CoRR (2022)
Virat Shejwalkar, Amir Houmansadr
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. NDSS (2021)
Virat Shejwalkar, Amir Houmansadr
Membership Privacy for Machine Learning Models Through Knowledge Transfer. AAAI (2021)
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning. CoRR (2021)
Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr
FSL: Federated Supermask Learning. CoRR (2021)
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 (2021)
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Quantifying Privacy Leakage in Graph Embedding. CoRR (2020)
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning. CoRR (2020)
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) (2020)
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Quantifying Privacy Leakage in Graph Embedding. MobiQuitous (2020)
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 (2019)
Hongyan Chang, Virat Shejwalkar, Reza Shokri, Amir Houmansadr
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. CoRR (2019)
Virat Shejwalkar, Amir Houmansadr, Hossein Pishro-Nik, Dennis Goeckel
Revisiting utility metrics for location privacy-preserving mechanisms. ACSAC (2019)
Virat Shejwalkar, Amir Houmansadr
Reconciling Utility and Membership Privacy via Knowledge Distillation. CoRR (2019)