In recent years, blockchain has received increasing attention and numerous applications have emerged from this technology. A renowned Blockchain application is the cryptocurrency Bitcoin, that has not only been effectively solving the double-spending problem but also it can confirm the legitimacy of transactional records without relying on a centralized system to do so. Therefore, any application using Blockchain technology as the base architecture ensures that the contents of its data are tamper-proof. This paper uses the decentralized Blockchain technology approach to ensure that consumers do not fully rely on the merchants to determine if products are genuine. We describe a decentralized Blockchain system with products anti-counterfeiting, in that way manufacturers can use this system to provide genuine products without having to manage direct-operated stores, which can significantly reduce the cost of product quality assurance.

Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical image segmentation is difficult due to the varying image quality among equipment. In this paper, the automatic method employed image multiscale intensity texture analysis and segmentation to solve this problem. In this paper, firstly, SVM is applied to identify common pneumothorax. Features are extracted from lung images with the LBP (local binary pattern). Then, classification of pneumothorax is determined by SVM. Secondly, the proposed automatic pneumothorax detection method is based on multiscale intensity texture segmentation by removing the background and noises in chest images for segmenting abnormal lung regions. The segmentation of abnormal regions is used for texture transformed from computing multiple overlapping blocks. The rib boundaries are identified with Sobel edge detection. Finally, in obtaining a complete disease region, the rib boundary is filled up and located between the abnormal regions.

The need for cloud servers for training deep neural network (DNN) models is increasing as more complex architecture designs of DNN models are developed. Nevertheless, cloud servers are considered semi-honest. With great attention to the privacy issues of medical diagnoses using a DNN, previous studies have proposed the idea of learnable image encryption. Though some methods have been presented to partially attack previous encryption schemes, there is still some space for improvement. We proposed a learnable image encryption scheme that is an enhanced version of previous methods and can be used to train a great DNN model and simultaneously keep the privacy of training images. We conducted an experiment on medical datasets from open sources and the result demonstrates the effectiveness of our proposed method in performance and privacy-preserving.

In the past, two models, pay-per-channel (PPC) and pay-per-view (PPV), for content access control in pay-TV broadcasting systems have been proposed. In PPC, a subscriber subscribes to one or multiple group(s) of channels, prearranged by the service provider, for a fixed period of time. Thus, it is unfair for the subscriber because the subscriber can not subscribe an arbitrary combination of channels according to his/her preference, and can not dynamically unsubscribe/change his/her subscription during the subscription period. On the other hand, PPV specifies that a subscriber can pay for each single program; thus, it provides a fair service. However, compared with PPC, PPV makes the subscriber inconvenient because of the high subscription frequency and low flexibility of channel selection. Moreover, all the existing PPV systems are only suitable for a small-scale Pay-TV system with limited users because of the overhead for transmission and storage. In this paper, we define a new model, flexible-PPC (F-PPC), which combines the properties and the advantages of PPC and PPV. F-PPC is an improved model of PPC for its fairness. It allows a subscriber to subscribe freely to his/her favorite channels, and unsubscribe/change his/her subscription at any time. Furthermore, it eliminates the inconvenience in PPV, and is suitable for a large-scale Pay-TV system with numerous subscribers and channels. In order to fulfill the requirements of F-PPC, we propose a new conditional access system (CAS) with a four-level key hierarchy. The proposed CAS utilizes a structure of a binary tree to update keys rapidly; therefore, the service provider can promptly update necessary keys when the subscribers change their subscriptions or leave the system. Compared with the existing CAS systems, our CAS is more efficient and flexible to support F-PPC.