Privacy-preserving PKI design based on group signature

Nowadays, Internet becomes a part of our life. We can make use of numerous services with personal computer, Lap-top, tablet, smart phone or smart TV. These devices with network make us enjoy ubiquitous computing life. Sometimes, on-line services request us authentication or identification for access control and authorization, and PKI technology is widely used because of its security. However the possibility of privacy invasion will increase, if We’re identified with same certificate in many services and these identification data are accumulated. For privacy-preserving authentication or anonymous authentication, there have been many researches such as Group signatures, anonymous credentials, etc. Among these researches, group signatures are very practical Because they provide unlinkability and traceability as well as anonymity. In this paper, we propose a privacy-preserving PKI based on group signature, with which users’ privacy can be Kept in services. Because of traceability, their identities can be traced if they abuse anonymity such as cybercrime. Moreover, we will also discuss open issues for further studies

EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets