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Deep Learning with Differential Privacy

Open Sourced By: Xin Pan

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.

paper: https://arxiv.org/abs/1607.00133

This repository contains code to create a setup for learning privacy-preserving student models by transferring knowledge from an ensemble of teachers trained on disjoint subsets of the data for which privacy guarantees are to be provided.

Knowledge acquired by teachers is transferred to the student in a differentially private manner by noisily aggregating the teacher decisions before feeding them to the student during training.

paper: https://arxiv.org/abs/1610.05755