215 research outputs found
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
While machine learning has achieved remarkable results in a wide variety of
domains, the training of models often requires large datasets that may need to
be collected from different individuals. As sensitive information may be
contained in the individual's dataset, sharing training data may lead to severe
privacy concerns. Therefore, there is a compelling need to develop
privacy-aware machine learning methods, for which one effective approach is to
leverage the generic framework of differential privacy. Considering that
stochastic gradient descent (SGD) is one of the mostly adopted methods for
large-scale machine learning problems, two decentralized differentially private
SGD algorithms are proposed in this work. Particularly, we focus on SGD without
replacement due to its favorable structure for practical implementation. In
addition, both privacy and convergence analysis are provided for the proposed
algorithms. Finally, extensive experiments are performed to verify the
theoretical results and demonstrate the effectiveness of the proposed
algorithms
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
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