Learning Light-Weight Edge-Deployable Privacy Models
Published: Dec 1, 2018
Abstract
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as...
Paper Details
Title
Learning Light-Weight Edge-Deployable Privacy Models
Published Date
Dec 1, 2018
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