Privacy Preserving Machine Learning
The focus of this work is learning models while keeping the data and/or the learned models private.
Selected Publications
- Measuring Data Leakage in Machine-Learning Models with Fisher Information
Awni Hannun, Chuan Guo, Laurens van der Maaten. UAI 2021.
(paper,
code,
slides)
Best Paper at UAI 2021 - Secure multiparty computations in floating-point arithmetic Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu. Information and Inference, 2021. (paper, code)
- Data Appraisal Without Data Sharing Mimee Xu, Laurens van der Maaten, Awni Hannun. NeurIPS PPML Workshop, 2020. (paper)
- The Trade-Offs of Private Prediction Laurens van der Maaten*, Awni Hannun*. arXiv:2007.05089, 2020. (paper, code)
- Certified Data Removal from Machine Learning Models Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten. ICML 2020. (paper, code)
- Privacy-Preserving Multi-Party Contextual Bandits Awni Hannun, Brian Knott, Shubho Sengupta, Laurens van der Maaten. arXiv:1910.05299 2019. (paper, code)