1,661 research outputs found
Releasing Graph Neural Networks with Differential Privacy Guarantees
With the increasing popularity of graph neural networks (GNNs) in several
sensitive applications like healthcare and medicine, concerns have been raised
over the privacy aspects of trained GNNs. More notably, GNNs are vulnerable to
privacy attacks, such as membership inference attacks, even if only black-box
access to the trained model is granted. We propose PrivGNN, a
privacy-preserving framework for releasing GNN models in a centralized setting.
Assuming an access to a public unlabeled graph, PrivGNN provides a framework to
release GNN models trained explicitly on public data along with knowledge
obtained from the private data in a privacy preserving manner. PrivGNN combines
the knowledge-distillation framework with the two noise mechanisms, random
subsampling, and noisy labeling, to ensure rigorous privacy guarantees. We
theoretically analyze our approach in the Renyi differential privacy framework.
Besides, we show the solid experimental performance of our method compared to
several baselines adapted for graph-structured data. Our code is available at
https://github.com/iyempissy/privGnn.Comment: Published in TMLR 202
Statistical Properties of Height of Japanese Schoolchildren
We study height distributions of Japanese schoolchildren based on the
statictical data which are obtained from the school health survey by the
ministry of education, culture, sports, science and technology, Japan . From
our analysis, it has been clarified that the distribution of height changes
from the lognormal distribution to the normal distribution in the periods of
puberty.Comment: 2 pages, 2 figures, submitted to J. Phys. Soc. Jpn.; resubmitted to
J. Phys. Soc. Jpn. after some revisio
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Graph neural networks (GNNs) have shown promising results on real-life
datasets and applications, including healthcare, finance, and education.
However, recent studies have shown that GNNs are highly vulnerable to attacks
such as membership inference attack and link reconstruction attack.
Surprisingly, attribute inference attacks has received little attention. In
this paper, we initiate the first investigation into attribute inference attack
where an attacker aims to infer the sensitive user attributes based on her
public or non-sensitive attributes. We ask the question whether black-box
attribute inference attack constitutes a significant privacy risk for
graph-structured data and their corresponding GNN model. We take a systematic
approach to launch the attacks by varying the adversarial knowledge and
assumptions. Our findings reveal that when an attacker has black-box access to
the target model, GNNs generally do not reveal significantly more information
compared to missing value estimation techniques. Code is available
Private Graph Extraction via Feature Explanations
Privacy and interpretability are two important ingredients for achieving
trustworthy machine learning. We study the interplay of these two aspects in
graph machine learning through graph reconstruction attacks. The goal of the
adversary here is to reconstruct the graph structure of the training data given
access to model explanations. Based on the different kinds of auxiliary
information available to the adversary, we propose several graph reconstruction
attacks. We show that additional knowledge of post-hoc feature explanations
substantially increases the success rate of these attacks. Further, we
investigate in detail the differences between attack performance with respect
to three different classes of explanation methods for graph neural networks:
gradient-based, perturbation-based, and surrogate model-based methods. While
gradient-based explanations reveal the most in terms of the graph structure, we
find that these explanations do not always score high in utility. For the other
two classes of explanations, privacy leakage increases with an increase in
explanation utility. Finally, we propose a defense based on a randomized
response mechanism for releasing the explanations, which substantially reduces
the attack success rate. Our code is available at
https://github.com/iyempissy/graph-stealing-attacks-with-explanationComment: Accepted at PETS 202
Contents
We introduce the `displacemon' electromechanical architecture that comprises
a vibrating nanobeam, e.g. a carbon nanotube, flux coupled to a superconducting
qubit. This platform can achieve strong and even ultrastrong coupling enabling
a variety of quantum protocols. We use this system to describe a protocol for
generating and measuring quantum interference between two trajectories of a
nanomechanical resonator. The scheme uses a sequence of qubit manipulations and
measurements to cool the resonator, apply an effective diffraction grating, and
measure the resulting interference pattern. We simulate the protocol for a
realistic system consisting of a vibrating carbon nanotube acting as a junction
in a superconducting qubit, and we demonstrate the feasibility of generating a
spatially distinct quantum superposition state of motion containing more than
nucleons.Comment: 12 pages, 7 figure
Are You Tampering With My Data?
We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary.Comment: 18 page
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