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About A Generic Framework For Privacy Preserving Deep Learning: Member of The Openmined Community
This document proposes a framework for privacy-preserving deep learning. It introduces the concept of tensor chains to represent operations like sending tensors to other workers. This allows implementing techniques like federated learning, secure multiparty computation, and differential privacy while exposing a familiar deep learning API. Early results on housing and diabetes datasets show the privacy features do not impact accuracy, though the framework introduces significant overhead that will be addressed. The framework is intended to popularize privacy techniques by making them accessible through common deep learning tools.
Detailed Information
| Author: | ['CHITHRA RAJ N'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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