Crowd-ML: A library for privacy-preserving machine learning on smart devices

Jihun Hamm, Jackson Luken, Yani Xie
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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 privacypreserving 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
more » ... c 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.
doi:10.1109/icassp.2017.7953387 dblp:conf/icassp/HammLX17 fatcat:dqkz7uuyyva47moppvqharste4