Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning von Shejwalkar, Virat, Houmansadr, Amir, Kairouz, Peter, Ramage, Daniel

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Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms von Takbiri, Nazanin, Shejwalkar, Virat, Houmansadr, Amir, Goeckel, Dennis L., Pishro-Nik, Hossein

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Machine Learning with Differentially Private Labels: Mechanisms and Frameworks von Tang, Xinyu, Nasr, Milad, Mahloujifar, Saeed, Shejwalkar, Virat, Song, Liwei, Houmansadr, Amir, Mittal, Prateek

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The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning von Shejwalkar, Virat, Lyu, Lingjuan, Houmansadr, Amir

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Membership Privacy for Machine Learning Models Through Knowledge Transfer von Shejwalkar, Virat, Houmansadr, Amir

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GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning von Duddu, Vasisht, Boutet, Antoine, Shejwalkar, Virat

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FRL: Federated Rank Learning von Mozaffari, Hamid, Shejwalkar, Virat, Houmansadr, Amir

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Security Analysis of SplitFed Learning von Khan, Momin Ahmad, Shejwalkar, Virat, Houmansadr, Amir, Anwar, Fatima Muhammad

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Quantifying Privacy Leakage in Graph Embedding von Duddu, Vasisht, Boutet, Antoine, Shejwalkar, Virat

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Membership Privacy for Machine Learning Models Through Knowledge Transfer von Shejwalkar, Virat, Houmansadr, Amir

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Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning von Shejwalkar, Virat, Houmansadr, Amir, Kairouz, Peter, Ramage, Daniel

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Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints von Shejwalkar, Virat, Ganesh, Arun, Mathews, Rajiv, Mu, Yarong, Song, Shuang, Thakkar, Om, Thakurta, Abhradeep, Zheng, Xinyi

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The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning von Shejwalkar, Virat, Lyu, Lingjuan, Houmansadr, Amir

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FRL: Federated Rank Learning von Mozaffari, Hamid, Shejwalkar, Virat, Houmansadr, Amir

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Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture von Tang, Xinyu, Mahloujifar, Saeed, Song, Liwei, Shejwalkar, Virat, Nasr, Milad, Houmansadr, Amir, Mittal, Prateek

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Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer von Chang, Hongyan, Shejwalkar, Virat, Shokri, Reza, Houmansadr, Amir

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Quantifying Privacy Leakage in Graph Embedding von Duddu, Vasisht, Boutet, Antoine, Shejwalkar, Virat

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Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints von Shejwalkar, Virat, Ganesh, Arun, Mathews, Rajiv, Mu, Yarong, Song, Shuang, Thakkar, Om, Thakurta, Abhradeep, Zheng, Xinyi

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Security Analysis of SplitFed Learning von Momin Ahmad Khan, Shejwalkar, Virat, Houmansadr, Amir, Fatima Muhammad Anwar

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LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS von SHEJWALKAR, Virat Vishnu, THAKURTA, Abhradeep Guha, GANESH, Arun, THAKKAR, Om Dipakbhai

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