1 code implementation • 6 Feb 2023 • Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar
Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight.
no code implementations • 25 Sep 2022 • Vinith M. Suriyakumar, Ashia C. Wilson
We study the problem of deleting user data from machine learning models trained using empirical risk minimization.
no code implementations • 4 Jun 2022 • Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun
In this work, we show models that are personalized with group attributes can reduce performance at a group level.
no code implementations • 1 Dec 2021 • Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training.
no code implementations • 13 Oct 2020 • Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi
Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data.
no code implementations • MIDL 2019 • Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, Nipaporn Tewattanarat, Andrea Doria, Anna Goldenberg
Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics.