Shaohua Wan

NTRU Implementation of Efficient Privacy-Preserving Location-Based Querying in VANET

The key for location-based service popularization in vehicular environment is security and efficiency. However, due to the constrained resources in vehicle-mounted system and the distributed structure of fog computation, disposing of the conflicts between real-time implementation and user’s privacy remains an open problem. Aiming at synchronously preserving the position information for users as well as the data proprietorship of service provider, an efficient location-based querying scheme is proposed in this paper. We argue that a recent scheme proposed by Jannati and Bahrak is time-consuming and vulnerable against active adaptive corruptions. Thus accordingly, a postquantum secure oblivious transfer protocol is devised based on efficient NTRU cryptosystem, which then serves as the understructure of a complete location-based querying scheme in ad hoc manner. The security of our scheme is proved under universal composability frame, while performance analysis is also carried out to testify its efficiency.

Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

Because the existing approaches for diagnosing sensor networks lead to low precision and high complexity, a new fault detection mechanism based on support vector regression and neighbor coordination is proposed in this work. According to the redundant information about meteorological elements collected by a multisensor, a fault prediction model is built using a support vector regression algorithm, and it achieves residual sequences. Then, the node status is identified by mutual testing among reliable neighbor nodes. Simulations show that when the sensor fault probability in wireless sensor networks is 40%, the detection accuracy of the proposed algorithm is over 87%, and the false alarm ratio is below 7%. The detection accuracy is increased by up to 13%, in contrast to other algorithms. This algorithm not only reduces the communication to sensor nodes but also has a high detection accuracy and a low false alarm ratio. The proposed algorithm is suitable for fault detection in meteorological sensor networks with low node densities and high failure ratios.

CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing

With the explosion of social data comes a great challenge called information overloading. To overcome this challenge, recommender systems are expected to support users in quickly accessing the appropriate content. However, cold-start users are a formidable challenge in the design of recommender systems because the conventional recommendation services are based on a single data source, namely, a single field. Considering the advantages of social-based and cross-domain approaches involving further additional data, we propose a cross-domain recommender system, including three approaches, based on multi-source social big data. The proposed approach is expected to effectively alleviate the issues of cold-start users by transferring user preferences from a related auxiliary domain to a target domain. Moreover, the transferred preferences are able to improve the diversity of recommendations. Through adequate evaluations based on an actual dataset in the book and music domains, it is shown that the accuracies of the three proposed approaches are significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization. In particular, the proposed approaches are available to provide cold-start users with highly effective recommendations.

Deep convolutional neural networks for diabetic retinopathy detection by image classification

Abstract Diabetic retinopathy (DR) is a common complication of diabetes and one of the major causes of blindness in the active population. Many of the complications of DR can be prevented by blood glucose control and timely treatment. Since the varieties and the complexities of DR, it is really difficult for DR detection in the time-consuming manual diagnosis. This paper is to attempt towards finding an automatic way to classify a given set of fundus images. We bring convolutional neural networks (CNNs) power to DR detection, which includes 3 major difficult challenges: classification, segmentation and detection. Coupled with transfer learning and hyper-parameter tuning, we adopt AlexNet, VggNet, GoogleNet, ResNet, and analyze how well these models do with the DR image classification. We employ publicly available Kaggle platform for training these models. The best classification accuracy is 95.68% and the results have demonstrated the better accuracy of CNNs and transfer learning on DR image classification.