137,911 research outputs found
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the output; and (3) It could be applied in a variety of different
deep neural networks. To achieve this, we figure out a way to perturb affine
transformations of neurons, and loss functions used in deep neural networks. In
addition, our mechanism intentionally adds "more noise" into features which are
"less relevant" to the model output, and vice-versa. Our theoretical analysis
further derives the sensitivities and error bounds of our mechanism. Rigorous
experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
highly effective and outperforms existing solutions.Comment: IEEE ICDM 2017 - regular pape
From the Information Bottleneck to the Privacy Funnel
We focus on the privacy-utility trade-off encountered by users who wish to
disclose some information to an analyst, that is correlated with their private
data, in the hope of receiving some utility. We rely on a general privacy
statistical inference framework, under which data is transformed before it is
disclosed, according to a probabilistic privacy mapping. We show that when the
log-loss is introduced in this framework in both the privacy metric and the
distortion metric, the privacy leakage and the utility constraint can be
reduced to the mutual information between private data and disclosed data, and
between non-private data and disclosed data respectively. We justify the
relevance and generality of the privacy metric under the log-loss by proving
that the inference threat under any bounded cost function can be upper-bounded
by an explicit function of the mutual information between private data and
disclosed data. We then show that the privacy-utility tradeoff under the
log-loss can be cast as the non-convex Privacy Funnel optimization, and we
leverage its connection to the Information Bottleneck, to provide a greedy
algorithm that is locally optimal. We evaluate its performance on the US census
dataset
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
Our everyday interactions with pervasive systems generate traces that capture
various aspects of human behavior and enable machine learning algorithms to
extract latent information about users. In this paper, we propose a machine
learning interpretability framework that enables users to understand how these
generated traces violate their privacy
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