Virat Shejwalkar

According to our database, Virat Shejwalkar authored at least 19 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks.

Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr

Proceedings of the 32nd USENIX Security Symposium, 2023

On the Pitfalls of Security Evaluation of Robust Federated Learning.

Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima M. Anwar

Proceedings of the 2023 IEEE Security and Privacy Workshops (SPW), 2023

The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning.

Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Machine Learning with Differentially Private Labels: Mechanisms and Frameworks.

Xinyu Tang, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, Prateek Mittal

Proc. Priv. Enhancing Technol., 2022

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints.

Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta

CoRR, 2022

Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture.

Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal

Proceedings of the 31st USENIX Security Symposium, 2022

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning.

Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

Security Analysis of SplitFed Learning.

Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr, Fatima Muhammad Anwar

Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, 2022

Towards privacy aware deep learning for embedded systems.

Vasisht Duddu, Antoine Boutet, Virat Shejwalkar

Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

2021

FSL: Federated Supermask Learning.

Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr

CoRR, 2021

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning.

Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage

CoRR, 2021

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning.

Virat Shejwalkar, Amir Houmansadr

Proceedings of the 28th Annual Network and Distributed System Security Symposium, 2021

Membership Privacy for Machine Learning Models Through Knowledge Transfer.

Virat Shejwalkar, Amir Houmansadr

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms.

Nazanin Takbiri, Virat Shejwalkar, Amir Houmansadr, Dennis L. Goeckel, Hossein Pishro-Nik

IEEE Wirel. Commun. Lett., 2020

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning.

Vasisht Duddu, Antoine Boutet, Virat Shejwalkar

CoRR, 2020

Quantifying Privacy Leakage in Graph Embedding.

Vasisht Duddu, Antoine Boutet, Virat Shejwalkar

Proceedings of the MobiQuitous '20: Computing, 2020

2019

Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer.

Hongyan Chang, Virat Shejwalkar, Reza Shokri, Amir Houmansadr

CoRR, 2019

Reconciling Utility and Membership Privacy via Knowledge Distillation.

Virat Shejwalkar, Amir Houmansadr

CoRR, 2019

Revisiting utility metrics for location privacy-preserving mechanisms.

Virat Shejwalkar, Amir Houmansadr, Hossein Pishro-Nik, Dennis Goeckel

Proceedings of the 35th Annual Computer Security Applications Conference, 2019