Crowd-ML: A library for privacy-preserving machine learning on smart devices
Jihun Hamm, Jackson Luken, Yani Xie · 2017
When user-generated data such as audio and video signals are used to train machine learning algorithms, users' privacy must be considered before the learned model is released. In this work, we present an open-source library for privacy-preserving machine learning framework on smart devices. The library allows Android and iOS devices to collectively learn a common classifier/regression model from distributed data with differential privacy, using a variant of minibatch stochastic gradient descent method. The library allows researchers and developers to easily implement and deploy customized tasks that use on-device sensors to collect sensitive data for machine learning.