| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning V Shejwalkar, A Houmansadr Network and Distributed System Security Symposium, NDSS, 2021 | 487 | 2021 |
| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning V Shejwalkar, A Houmansadr, P Kairouz, D Ramage IEEE Symposium on Security and Privacy, 2022 | 323 | 2022 |
| Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer H Chang, V Shejwalkar, R Shokri, A Houmansadr NeurIPS Workshop on New Frontiers in Federated Learning, 2021 | 198 | 2021 |
| Quantifying Privacy Leakage in Graph Embedding V Duddu, A Boutet, V Shejwalkar EAI MobiQuitous, 2021 | 135 | 2021 |
| Membership Privacy for Machine Learning Models Through Knowledge Transfer V Shejwalkar, A Houmansadr AAAI, 2021 | 111 | 2021 |
| Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture X Tang, S Mahloujifar, L Song, V Shejwalkar, M Nasr, A Houmansadr, ... USENIX Security Symposium, 2022 | 96 | 2022 |
| Membership inference attacks against nlp classification models V Shejwalkar, HA Inan, A Houmansadr, R Sim NeurIPS 2021 Workshop Privacy in Machine Learning, 2021 | 62 | 2021 |
| FRL: Federated Rank Learning H Mozaffari, V Shejwalkar, A Houmansadr USENIX Security Symposium, 2023 | 38* | 2023 |
| Security analysis of splitfed learning MA Khan, V Shejwalkar, A Houmansadr, FM Anwar Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems …, 2022 | 19 | 2022 |
| Reconciling utility and membership privacy via knowledge distillation V Shejwalkar, A Houmansadr arXiv e-prints, arXiv: 1906.06589, 2019 | 17 | 2019 |
| Machine Learning with Differentially Private Labels: Mechanisms and Frameworks X Tang, M Nasr, S Mahloujifar, V Shejwalkar, L Song, A Houmansadr, ... Proceedings on Privacy Enhancing Technologies 1, 19, 2022 | 16 | 2022 |
| The perils of learning from unlabeled data: Backdoor attacks on semi-supervised learning V Shejwalkar, L Lyu, A Houmansadr International Conference on Computer Vision (ICCV), 2023 | 11 | 2023 |
| Towards privacy aware deep learning for embedded systems V Duddu, A Boutet, V Shejwalkar Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, 520-529, 2022 | 11* | 2022 |
| On the pitfalls of security evaluation of robust federated learning MA Khan, V Shejwalkar, A Houmansadr, FM Anwar 2023 IEEE Security and Privacy Workshops (SPW), 57-68, 2023 | 8 | 2023 |
| Recycling scraps: Improving private learning by leveraging intermediate checkpoints V Shejwalkar, A Ganesh, R Mathews, Y Mu, S Song, O Thakkar, ... arXiv preprint arXiv:2210.01864, 2022 | 7 | 2022 |
| Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer C Hongyan, S Virat, S Reza, H Amir arXiv preprint arXiv:1912.11279, 2019 | 6 | 2019 |
| Leveraging prior knowledge asymmetries in the design of location privacy-preserving mechanisms N Takbiri, V Shejwalkar, A Houmansadr, DL Goeckel, H Pishro-Nik IEEE Wireless Communications Letters 9 (11), 2005-2009, 2020 | 3 | 2020 |
| Revisiting utility metrics for location privacy-preserving mechanisms V Shejwalkar, A Houmansadr, H Pishro-Nik, D Goeckel Proceedings of the 35th Annual Computer Security Applications Conference …, 2019 | 3 | 2019 |
| Quantifying unintended memorization in best-rq asr encoders V Shejwalkar, O Thakkar, A Narayanan Interspeech 2024, 2905-2909, 2024 | 1 | 2024 |
| Quantifying and Enhancing the Security of Federated Learning VV Shejwalkar | 1 | 2023 |
Amir HoumansadrUniversity of Massachusetts AmherstVerified email at cs.umass.edu