A privacy‐preserving framework for smart context‐aware healthcare applications

Internet of things (IoT) is a disruptive paradigm with wide ranging applications including healthcare, manufacturing, transportation and retail. Within healthcare, smart connected wearable devices are widely used to achieve improved wellbeing, quality of life and security of citizens. Such connected devices generate significant amount of data containing sensitive information about patient requiring adequate protection and privacy assurance. Unauthorized access to an individual’s private data constitutes a breach of privacy leading to catastrophic outcomes for an individuals personal and professional life. Furthermore, breach of privacy may also lead to financial loss to the governing body such as those proposed as part of the General Data Protection Regulation (GDPR) in Europe. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability and pervasive processing, it also introduces challenges with respect to scalability, reliability and context-awareness for its applications. This paper is focused on privacy preservation within smart context-aware healthcare with a special emphasis on privacy assurance challenges within the Electronic Transfer of Prescription (ETP). To this extent, we present a case for a comprehensive, coherent, and dynamic privacypreserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models we propose an enhancement for the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for cutting edge IoT applications such as smart healthcare whilst addressing unique challenges with respect to context-aware mobility of such applications

IoT and Machine Learning Based Attacks Detection Model on Wearable Health Care Devices

The Internet of Things (IoT) in healthcare is becoming more and more popular in the field of research aimed at improving the effectiveness of intelligent healthcare networks and applications. Nonetheless, distinct risks affect the security and privacy of data in smart health (S-Health). IoT enables healthcare professionals to engage with patients more proactively and with greater vigilance. Smart gadgets with tiny sensors attached to them that communicate with one another to track each other\u27s performance are part of the Internet of Things. To defend S-Health from MITM attacks. The suggested method employs two layers of machine learning algorithms for attack detection and security mechanisms, including low-cost access policies for SHRs (Smart Health Records), lightweight IoT detection schemes, and timely detection of to lessen their impact on the network. According to simulation data, the suggested Hybrid ML performs better than current methods and has a higher attack detection rate overall. The main goal of this research article is to develop an attack detection technique

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics

Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs

Decentralising the Internet of Medical Things with Distributed Ledger Technologies and Off-Chain Storages: a Proof of Concept

The privacy issue limits the Internet of Medical Things. Medical information would enhance new medical studies, formulate new treatments, and deliver new digital health technologies. Solving the sharing issue will have a triple impact: handling sensitive information easily, contributing to international medical advancements, and enabling personalised care. A possible solution could be to decentralise the notion of privacy, distributing it directly to users. Solutions enabling this vision are closely linked to Distributed Ledger Technologies. This technology would allow privacy-compliant solutions in contexts where privacy is the first need through its characteristics of immutability and transparency. This work lays the foundations for a system that can provide adequate security in terms of privacy, allowing the sharing of information between participants. We introduce an Internet of Medical Things application use case called “Balance”, networks of trusted peers to manage sensitive data access called “Halo”, and eventually leverage Smart Contracts to safeguard third party rights over data. This architecture should enable the theoretical vision of privacy-based healthcare solutions running in a decentralised manner

Information Privacy Concerns in the Age of Internet of Things

Internet of things (IoT) offer new opportunities for advancement in many domains including healthcare, home automation, manufacturing and transportation. In recent years, the number of IoT devices have exponentially risen and this meteoric rise is poised to continue according to the industry. Advances in the IoT integrated with ambient intelligence are intended to make our lives easier. Yet for all these advancements, IoT also has a dark side. Privacy and security were already priorities when personal computers, devices and work stations were the only point of vulnerability to personal information, however, with the ubiquitous nature of smart technologies has increased data collection points around us exponentially. Beyond that, the massive amount of data collected by IoT devices is relatively unknown and uncontrolled by users thereby exacerbating privacy issues and concerns. This study aims to create better understanding of privacy concerns stemming from most popular smart technologies, categorizing the data collected by them. We investigate how the data collection raises information privacy concerns among users of IoT

Progression and Challenges of IoT in Healthcare: A Short Review

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.Comment: 7 page

Scalable, Efficient and Precise Natural Language Processing in the Semantic Web

The Internet of Things (IoT) is an emerging phenomenon in the public space. Users with accessibility needs could especially benefit from these “smart” devices if they were able to interact with them through speech. This thesis presents a Compositional Semantics and framework for developing extensible and expressive Natural Language Query Interfaces to the Semantic Web, addressing privacy and auditability needs in the process. This could be particularly useful in healthcare or legal applications, where confidentiality of information is a key concer