Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

@article{Hamm2015CrowdMLAP,
  title={Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices},
  author={Jihun Hamm and Adam C. Champion and Guoxing Chen and Mikhail Belkin and Dong Xuan},
  journal={2015 IEEE 35th International Conference on Distributed Computing Systems},
  year={2015},
  pages={11-20}
}
Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. Crowds of smart devices offer opportunities to collectively sense and perform computing tasks at an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowd sensing data with differential privacy guarantees. Crowd-ML endows a crowd sensing… CONTINUE READING
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