SWAN: A Framework to Bootstrap Trust in Network Data Science
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Date
2024-08-07
Authors
Elfandi, Abduarraheem
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
Two significant challenges must be overcome before machine learning models can be deployed in an operational setting: the ability to achieve trust within and across enclaves which includes addressing data privacy concerns. In this thesis, we propose SWAN, a framework to tackle these challenges by allowing data to be labeled at scale, achieving trust within an enclave by providing insight into black-box machine learning models through a hybrid explainability technique which is done by utilizing the combination of global and local interpretability techniques. Furthermore, the framework allows for collaboration across enclaves while maintaining data privacy requirements. This thesis includes unpublished co-authored material by Ramakrishnan Durairajan and Walter Willinger.