Understand
We detail a new framework for privacy preserving deep learning and discuss its assets.
- The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors.
- This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user.
- We report early results on the Boston Housing and Pima Indian Diabetes datasets.
Built on
Pima indian diabetes dataset
Vincent Sigillito · 1990
Earlier work this paper cites.
Multiparty computation from somewhat homomorphic encryption
Ivan Damgård, Valerio Pastro, Nigel Smart, and Sarah Zakarias · 2012
Earlier work this paper cites.
Similar
Practical covertly secure mpc for dishonest majority–or: breaking the spdz limits
Ivan Damgård, Marcel Keller, Enrique Larraia, Valerio Pastro, Peter Scholl, and Nigel P Smart · 2013
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Then
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar · 2016
Later among the works it cites.
Safetynets: Verifiable execution of deep neural networks on an untrusted cloud
Zahra Ghodsi, Tianyu Gu, and Siddharth Garg · 2017
Later among the works it cites.
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