# Emerging Information Technologies for Next Generation Communications and Networks
###### Abstract
The popularity of wearable devices equipped with a variety of sensors that can measure users' health status and monitor their lifestyles has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. The health data collected via wearable devices are useful for providing healthcare service. However, the indiscriminate collection of an individual's health data raises serious privacy concerns, because the health data obtained by wearable devices contain sensitive and private information related to the wearer's personal health and lifestyle. The authors in [3] propose a method in order to securely preserve the data from the wearable devices. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. Particularly, this is to mitigate the error incurred by the perturbation mechanism of location differential privacy, at first. The proposed scheme samples a small amount of salient data that represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. The authors performed extensive experiments and experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach. In recent daily life, video has become the most important medium for communication among people. Therefore, reversible data hiding technologies for video, which inserts and extracts data without compromising the original content, have been developed. Therefore, some important and private information can be hidden in the video without damaging the original video in order to be used in the copyright protection and distribution field of video. A paper proposes a practical and genuine reversible data hiding method by adopting a multidimensional histogram shifting scheme on quantized discrete cosine transform (QDCT) coefficients in the H.264/AVC bitstream [4]. The proposed method defines the vacant histogram bins as a set of n-dimensional vectors and finds the optimal vector space, which gives the best performance, in a 4 \(\times\) 4 QDCT block. In addition, the secret message is mapped to the optimal vector space, which is equivalent to embedding the information into the QDCT block. The proposed method is comparatively evaluated in terms of Peak Signal-to-Noise Ratio(PSNR), Payload Per Frame (PFF), and File Increases Per Payload (FPP). The evaluation results show that the efficiency of the proposed hiding method is higher than the other compared five existing methods in terms of PSNR, PFF, and FPP. In addition, the image distortion and maximum payload capacity are measured quite high. Attackers use a variety of techniques to insert redirection JavaScript in order to lead a user to a malicious webpage, in which a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. The authors in [5] propose a lightweight detection system based on static analysis to classify the obfuscation type and to promptly detect the obfuscated redirection JavaScript. The proposed model detects the obfuscated redirection JavaScript by converting the JavaScript into an abstract syntax tree (AST). Then, the structure and token information are extracted. Specifically, the paper proposes a lightweight AST to identify the obfuscation type and the revised term frequency-inverse document frequency to efficiently detect the malicious redirection JavaScript. The proposed method enables rapid identification of the obfuscated redirection JavaScript and proactive blocking of the webpages that are used in drive-by-download attacks.