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Graph Neural Networks (GNNs), which generalize traditional deep neural networks or graph data, have achieved state of the art performance on several graph analytical tasks like node classification, link prediction or graph classification. We focus on how trained GNN models could leak information about the member nodes that they were trained on. In particular, we focus on answering the question: given a graph, can we determine which nodes were used for training the GNN model? We operate in the inductive settings for node classification, which means that none of the nodes in the test set (or the non-member nodes) were seen during the training. We propose a simple attack model which is able to distinguish between the member and non-member nodes while just having a black-box access to the model. We experimentally compare the privacy risks of four representative GNN models. Our results show that all the studied GNN models are vulnerable to privacy leakage. While in traditional machine learning models, overfitting is considered the main cause of such leakage, we show that in GNNs the additional structural information is the major contributing factor. CCS CONCEPTS• Computing methodologies → Neural networks; • Security and privacy → Privacy protections.

Due to the increasing number of mobile robots including domestic robots for cleaning and maintenance in developed countries, human activity recognition is inevitable for congruent human-robot interaction. Needless to say that this is indeed a challenging task for robots, it is expedient to learn human activities for autonomous mobile robots (AMR) for navigating in an uncontrolled environment without any guidance. Building a correct classifier for complex human action is nontrivial since simple actions can be combined to recognize a complex human activity. In this paper, we trained a model for human activity recognition using convolutional neural network. We trained and validated the model using the Vicon physical action dataset and also tested the model on our generated dataset (VMCUHK). Our experiment shows that our method performs with high accuracy, human activity recognition task both on the Vicon physical action dataset and VMCUHK dataset.

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Privacy and interpretability are two of the 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, perturbationbased, 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 anonymized code is available at xxxxxxxxx.