Professional Information

Interviewing and Recruiting

Attribute-to-Delete: Machine Unlearning via Datamodel Matching
- R. Rinberg, K. Georgiev, S. Park, S. Garg, A. Ilyas, S. Neel, A. Madry. [Full paper on Arxiv. Workshop accepted to GenLaw 2024.]
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Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning. Preprint. Updated working draft available upon request.
- R. Rinberg, I. Shumailov, R. Cummings, N. Papernot. [Pre-print]
Have it your way: Individualized Privacy Assignment for DP-SGD
- F. Boenisch, C. Mühl, A. Dziedzic, R. Rinberg, N. Papernot. [Accepted to Neurips 2023]
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees.
- F. Boenisch, C Mühl, R. Rinberg, J. Ihrig, A. Dziedzic. Accepted to PoPETs 2023.
“Privacy when Everyone is Watching: An SOK on Anonymity on the Blockchain”..
- R. Rinberg, N. Agarwal. 2022.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation, 2022.
“Creolizing the Web”.
- A. Tamaskar, R. Rinberg, S. Chakraborty, B. Mishra. 2021.

I lead a group of cross-university graduate students who meet and discuss privacy and security from technical, legal, and policy perspectives. We are called Technically Private (though informally sometimes go by Privacy Peeps). Please reach out if you are interested in joining.