Dear Colleagues,

Security and privacy are vital challenges for IoT users as they move around denser and more ubiquitous, networked, intelligent environments embedded with different connected smart objects that interact with each other and with IoT users in new ways. The focus is on the use of devices in passive, non-smart environments, with far less focus on emerging and future security and privacy threats for the IoT. Increasingly, it is difficult to differentiate which physical spaces are smart whether they are personal, social, or public in scope and what the different privacy invasions and security threats are, e.g., the use of remotely connected virtual digital assistants that now increasingly encroach on personal spaces. Most security and privacy researchers predominantly focus on the current information security threat landscape, protecting the use of personal smart tab and smart pad sized devices, such as smartphones, tablets, and laptops. However, the range of smart objects also encompasses smart skins (wearable body shaped devices, e.g., wrist-ware, glasses), smart dust (tiny smart components), smart boards (whose displays adapt to multiple local users), same clay (aggregates of smart devices), and smart (body-sized or greater-sized) containers.

IoT objects collect and aggregate fragments of data that relate to their service, and IoT users often have to submit complete contextual information to providers to access their mobile information services, hence, IoT users have a lack of user-centered control of their information privacy. Some lower computation devices, such as eHealth wearables, tend to support weaker security as these often use open network connections or ones with weaker security, leading to higher security risks such as eavesdropping. Not only can IoT users be tracked by remote service providers but as physical environments become more intelligent, these physical spaces increasingly contain more smart devices that can also track users. When multiple devices are networked as an Internet of Things, multi-environment context collusions can be used to identify and track individuals in a way that violates their privacy expectations.

Sophisticated artificial intelligence (AI) mechanisms not only enhance the reliability of IoT networks but also increase the security and privacy problems as AI mechanisms require accessing more data from IoT devices to get better results. The IoT instrumented-self offers challenges because of the use of microelectromechanical system sensors such as accelerometers embedded in phones and wearables, hiding the computing yet imbuing them with the ability to acquire more fine-grained user context information. This is accompanied by a rise in information services to share this and in crowd-sensing data applications that can data mine IoT users’ information to identify individual users’ unique behaviors.

This Special Issue seeks submissions offering research and development systems, applications, results, and experimental solutions that advance the state of the art of security and privacy solutions for IoT systems in Intelligent Environments comprising ubiquitous computing devices connected into Internets of Things. We seek articles that advance security and privacy for IoT users beyond mere mobile phone device use. We especially welcome papers that tackle both security and privacy for smart IoT users in intelligent environments and that use AI mechanisms to improve the security and privacy of IoT systems.

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• Security
• Privacy
• Internet of Things
• Artificial Intelligence
• Smart IoT Environments
• Intelligent IoT Environments

Title: --
Authors: Ana Maria de Almeida
Affiliation: --
Abstract: Nowadays, we have been witnessing a growing trend in the usage of applications that acquire and collect data from sensors built in mobile and wearable devices in a daily basis and from everywhere on the earth. Data flows in and out these devices and through the cloud, raising concern towards its security and privacy. This work proposes an architecture for secure and auditable exchange of personal and special information using an intelligent mobile application connected with a central management system core. This framework is based on a distributed micro-services architecture that implements multiple security services, namely secure storage of private health-related information and user-centric access control. Moreover, the system uses blockchain technology for controlling and audit the access to user data. The system also implements a Federated Learning Model that is customised by local computation on the edge and may send, under permission, data pertaining the model auto adjusting parameters to the central system point or processed data to authorized third-parties. A particular mobile health application using the proposed secure and intelligent architecture will be detailed