Assisting traffic control is one of the most important applications on Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy-preservation of this information are critical while conflicted with each other in vehicular communication. In this paper, we propose a semi-centralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy-preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes like locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy-preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical. Index Terms-attribute-based encryption, blockchain, privacy preserving, internet of vehicles.
Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has been carried out for studying the reasons of DNNs' vulnerability to adversarial perturbation and focused on model architecture. No research has been done on investigating the impact of optimization algorithms (namely, optimizers in DNNs) employed in training DNN models on models' sensitivity to adversarial examples. This paper aims to study this impact from an experimental perspective. We analyze the sensitivity of a model not only from the aspect of white-box and black-box attack setups, but also from the aspect of different types of datasets. Four common optimizers, SGD, RMSprop, Adadelta, and Adam, are investigated on structured and unstructured datasets. Extensive experiment results indicate that an optimization algorithm does pose effects on the DNN model sensitivity to adversarial examples. That is, when training models and generating adversarial examples, Adam optimizer can generate better quality adversarial examples for structured datasets, and Adadelta optimizer can generate better quality adversarial examples for unstructured datasets. In addition, the choice of optimizers does not affect the transferability of adversarial examples.
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