Internet of Things (IoT) has gradually become one of the most important platforms across different disciplines, by enabling dedicated physical objects to communicate with other Internet-enabled things. With this trend, more devices in medical environments are capable of connecting with each other, named Internet of Medical Things (IoMT). It aims for improving efficiency and reducing communication delay, e.g., monitoring the status of patients and notifying abnormal events. However, due to the distributed nature, insider attacks are still one of the major threats to such IoT environment. How to improve the trust management in IoMT remains a challenge. Motivated by the popularity of blockchain technology, in this paper, our general goal is to investigate the performance of blockchain-based trust management.In particular, we focus on a particular type of IoMT, named medical smartphone networks (MSNs), because of the wide adoption of smartphones in the medical domain. Then, we apply blockchains for enhancing the effectiveness of Bayesian inferencebased trust management to detect malicious nodes in MSNs. In the evaluation, we explore the performance of our approach in two different healthcare environments, and experimental results demonstrate that blockchain technology can help improve the detection efficiency of detecting malicious nodes with reasonable workload.

With the rapid development of information technology (IT) in financial industry, financial technology (FinTech) has become an emerging area for enterprises and organizations. Due to the wide adoption of IT, various FinTech applications are used by financial industry to help process information and offer financial services. Traditionally, textual passwords are the most widely deployed authentication mechanism, while having many known limitations. As a result, there is a need to enhance the security of FinTech authentication against cyber-criminals. As an alternative, graphical passwords (GPs) are considered as one promising solution to complement traditional password-based systems. In the literature, various GP schemes were proposed such as PassPoints, DAS, Cued Click Points, GeoPass, etc. In this work, we identify that multiple password inference has become a challenge for most GP schemes, and thus design RouteMap, a map-and route-based GP to further improve the security of FinTech applications. This scheme requires users to create a route on a world map as their credentials. In the evaluation, we involved a total of 120 participants, among which 60 of them have financial (FinTech) background, and investigated the performance of RouteMap by comparing it with two similar schemes. Our results demonstrate that participants can achieve better performance using RouteMap in the aspects of authentication accuracy and multiple password memory. Our effort attempts to complement existing studies and stimulate more research on the combination of GP and FinTech.