Revisiting utility metrics for location privacy-preserving mechanisms. - Virat Shejwalkar; Amir Houmansadr; Hossein Pishro-Nik; Dennis Goeckel (ACSAC, 2019)

Reconciling Utility and Membership Privacy via Knowledge Distillation. - Virat Shejwalkar; Amir Houmansadr (CoRR, 2019)

Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. - Hongyan Chang; Virat Shejwalkar; Reza Shokri; Amir Houmansadr (CoRR, 2019)

Quantifying Privacy Leakage in Graph Embedding. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (MobiQuitous, 2020)

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (CoRR, 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)

Membership Privacy for Machine Learning Models Through Knowledge Transfer. - Virat Shejwalkar; Amir Houmansadr (AAAI, 2021)

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. - Virat Shejwalkar; Amir Houmansadr (NDSS, 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)

FSL: Federated Supermask Learning. - Hamid Mozaffari; Virat Shejwalkar; Amir Houmansadr (CoRR, 2021)

Towards privacy aware deep learning for embedded systems. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (SAC, 2022)

Security Analysis of SplitFed Learning. - Momin Ahmad Khan; Virat Shejwalkar; Amir Houmansadr; Fatima M. Anwar 001 (SenSys, 2022)

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. - Virat Shejwalkar; Amir Houmansadr; Peter Kairouz; Daniel Ramage (SP, 2022)

Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture. - Xinyu Tang 003; Saeed Mahloujifar; Liwei Song; Virat Shejwalkar; Milad Nasr; Amir Houmansadr; Prateek Mittal (USENIX Security Symposium, 2022)

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Om Thakkar 001; Abhradeep Thakurta (CoRR, 2022)

Machine Learning with Differentially Private Labels: Mechanisms and Frameworks. - Xinyu Tang 003; Milad Nasr; Saeed Mahloujifar; Virat Shejwalkar; Liwei Song; Amir Houmansadr; Prateek Mittal (Proc. Priv. Enhancing Technol., 2022)

The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning. - Virat Shejwalkar; Lingjuan Lyu; Amir Houmansadr (ICCV, 2023)

On the Pitfalls of Security Evaluation of Robust Federated Learning. - Momin Ahmad Khan; Virat Shejwalkar; Amir Houmansadr; Fatima M. Anwar 001 (SP (Workshops), 2023)

Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. - Hamid Mozaffari; Virat Shejwalkar; Amir Houmansadr (USENIX Security Symposium, 2023)

Quantifying Unintended Memorization in BEST-RQ ASR Encoders. - Virat Shejwalkar; Om Thakkar 001; Arun Narayanan (INTERSPEECH, 2024)

Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Yarong Mu; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Xinyi Zheng (Proc. Priv. Enhancing Technol., 2025)

Decoding FL Defenses: Systemization, Pitfalls, and Remedies. - Momin Ahmad Khan; Virat Shejwalkar; Yasra Chandio; Amir Houmansadr; Fatima Muhammad Anwar 001 (CoRR, 2025)

Multilingual and Multi-Accent Jailbreaking of Audio LLMs. - Jaechul Roh; Virat Shejwalkar; Amir Houmansadr (CoRR, 2025)