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Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data.
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources.
Anonymizing Data for Privacy-Preserving Federated Learning
In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning.
Differential Privacy-enabled Federated Learning for Sensitive Health Data
We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
A Blockchain Framework for Managing and Monitoring Data in Multi-Site Clinical Trials
The cost of conducting multi-site clinical trials has significantly increased over time, with site monitoring, data management, and amendments being key drivers.
Databases Computers and Society
