A fog based middleware for automated compliance with OECD privacy principles in Internet of Healthcare Things

Cloud-based healthcare service with the Internet of Healthcare Things (IoHT) is a model for healthcare delivery for urban areas and vulnerable population that utilizes the digital communications and the IoHT to provide flexible opportunities to transform all the health data into workable, personalized health insights, and help attain wellness outside the traditional hospital setting. This model of healthcare Web services acts like a living organism, taking advantage of the opportunities afforded by running in cloud infrastructure to connect patients and providers anywhere and anytime to improve the quality of care, with the IoHT, acting as a central nervous system for this model that measures patients' vital statistics, constantly logging their health data, and report any abnormalities to the relevant healthcare provider. However, it is crucial to preserve the privacy of patients while utilizing this model so as to maintain their satisfaction and trust in the offered services. With the increasing number of cases for privacy breaches of healthcare data, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal health information. The health insurance portability and accountability act and the privacy principles established by the Organization for Economic Cooperation and Development (OECD) are examples of such regulation frameworks. In this paper, we assert that utilizing the cloud-based healthcare services to generate accurate health insights are feasible, while preserving the privacy of the end-users' sensitive health information, which will be residing on a clear form only on his/her own personal gateway. To support this claim, the personal gateways at the end-users' side will act as intermediate nodes (called fog nodes) between the IoHT devices and the cloud-based healthcare services. In such solution, these fog nodes will host a holistic privacy middleware that executes a two-stage concealment process within a distributed data collection protocol that utilizes the hierarchical nature of the IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. Additionally, the proposed solution complies with one of the common privacy regulation frameworks for fair information practice in a natural and functional way-which is OECD privacy principles. We depicted how the proposed approach can be integrated into a scenario related to preserving the privacy of the users' health data that is utilized by a cloud-based healthcare recommender service in order to generate accurate referrals. Our holistic approach induces a straightforward solution with accurate results, which are beneficial to both end-users and service providers

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Privacy in Internet of Things: from Principles to Technologies

Ubiquitous deployment of low-cost smart devices and widespread use of high-speed wireless networks have led to the rapid development of the Internet of Things (IoT). IoT embraces countless physical objects that have not been involved in the traditional Internet and enables their interaction and cooperation to provide a wide range of IoT applications. Many services in the IoT may require a comprehensive understanding and analysis of data collected through a large number of physical devices that challenges both personal information privacy and the development of IoT. Information privacy in IoT is a broad and complex concept as its understanding and perception differ among individuals and its enforcement requires efforts from both legislation as well as technologies. In this paper, we review the state-of-the-art principles of privacy laws, the architectures for IoT and the representative privacy enhancing technologies (PETs). We analyze how legal principles can be supported through a careful implementation of privacy enhancing technologies (PETs) at various layers of a layered IoT architecture model to meet the privacy requirements of the individuals interacting with IoT systems. We demonstrate how privacy legislation maps to privacy principles which in turn drives the design of necessary privacy enhancing technologies to be employed in the IoT architecture stack

Analysis of the security and privacy risks and challenges in smart cities' traffic light system

Currently, the IoT network is the fastest growing network in the world that brings the smart cities revolution. The increase in the smart cities development poses several security and privacy risks. With the acceleration of times, we can now hastily observe the lack of privacy in our life. The major security and privacy issues occur because of either non-consideration of its security and privacy aspects or having inappropriate controls in place. Many of these issues could be resolved by applying advanced IoT-enabled solutions. This paper presents security and privacy risks and challenges against issues within traffic lights system, which is a complex and critical smart cities system. The paper also addresses a proposed secure and privacy-aware system for future traffic light system

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD)