Adam Dziedzic

Postdoctoral Fellow

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Research Interests

Applied ML Trustworthy ML Robustness Adversarial ML ML in Healthcare Data Science Data Privacy Applied Differential Privacy FFT Databases

Papers

1. On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples
   Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot
   

   Paper BibTeX

   @article{adelin2021on,
     author = {Travers, Adelin and Licollari, Lorna and Wang, Guanghan and Chandrasekaran, Varun and Dziedzic, Adam and Lie, David and Papernot, Nicolas},
     title = {On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples},
     year = {2021}
   }
   

2. CaPC Learning: Confidential and Private Collaborative Learning
   Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
   In Proceedings of the 9th International Conference on Learning Representations

   Paper BibTeX

   @inproceedings{christopher2021capc,
     author = {Choquette-Choo, Christopher A. and Dullerud, Natalie and Dziedzic, Adam and Zhang, Yunxiang and Jha, Somesh and Papernot, Nicolas and Wang, Xiao},
     booktitle = {Proceedings of the 9th International Conference on Learning Representations},
     title = {CaPC Learning: Confidential and Private Collaborative Learning},
     year = {2021}
   }
   

3. When the Curious Abandon Honesty: Federated Learning Is Not Private
   Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
   

   Paper BibTeX

   @article{franziska2021when,
     author = {Boenisch, Franziska and Dziedzic, Adam and Schuster, Roei and Shamsabadi, Ali Shahin and Shumailov, Ilia and Papernot, Nicolas},
     title = {When the Curious Abandon Honesty: Federated Learning Is Not Private},
     year = {2021}
   }