Biblio

In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.

With the exponential growth in the usage of electronic medical records (EMR), the amount of data generated by the healthcare industry has too increased exponentially. These large amounts of data, known as “Big Data” is mostly unstructured. Special big data analytics methods are required to process the information and retrieve information which is meaningful. As patient information in hospitals and other healthcare facilities become increasingly electronic, Big Data technologies are needed now more than ever to manage and understand this data. In addition, this information tends to be quite sensitive and needs a highly secure environment. However, current security algorithms are hard to be implemented because it would take a huge amount of time and resources. Security protocols in Big data are also not adequate in protecting sensitive information in the healthcare. As a result, the healthcare data is both heterogeneous and insecure. As a solution we propose the Secure Pattern-Based Data Sensitivity Framework (PBDSF), that uses machine learning mechanisms to identify the common set of attributes of patient data, data frequency, various patterns of codes used to identify specific conditions to secure sensitive information. The framework uses Hadoop and is built on Hadoop Distributed File System (HDFS) as a basis for our clusters of machines to process Big Data, and perform tasks such as identifying sensitive information in a huge amount of data and encrypting data that are identified to be sensitive.

Emergency medical services universally get regarded as the essential part of the health care delivery system [1]. A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals. Nearly thirty million Americans do not live within an hour of trauma care, so this poor access to trauma centers links to higher pre-hospital death rates in more than half of the United States [2]. So, we need to address the problem. In a patient care-cycle, loads of medical data items are born in different healthcare settings using a disparate system of records during patient visits. The ability for medical care providers to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate. Today, the problem exists with a continuous recording system of the patient data between healthcare providers. In this paper, we've introduced a combination of secure file transfer methods/tools and blockchain technology as a solution to record patient Emergency relevant medical data as patient walk through from one clinic/medical facility to another, creating a continuous footprint of patient as a secure and scalable data source. So, ambulance crews can access and use it to provide high quality pre-hospital care. All concerns of medical record sharing and accessing like authentication, privacy, security, scalability and audibility, confidentiality has been considered in this approach.

Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.

A blockchain powered Health information ecosystem can solve a frequently discussed problem of the lifelong recorded patient health data, which seriously could hurdle the privacy of the patients and the growing data hunger of the research and policy maker institutions. On one side the general availability of the data is vital in emergency situations and supports heavily the different research, population health management and development activities, on the other side using the same data can lead to serious social and ethical problems caused by malicious actors. Currently, the regulation of the privacy data varies all over the world, however underlying principles are always defensive and protective towards patient privacy against general availability. The protective principles cause a defensive, data hiding attitude of the health system developers to avoid breaching the overall law regulations. It makes the policy makers and different - primarily drug - developers to find ways to treat data such a way that lead to ethical and political debates. In our paper we introduce how the blockchain technology can help solving the problem of secure data storing and ensuring data availability at the same time. We use the basic principles of the American HIPAA regulation, which defines the public availability criteria of health data, however the different local regulations may differ significantly. Blockchain's decentralized, intermediary-free, cryptographically secured attributes offer a new way of storing patient data securely and at the same time publicly available in a regulated way, where a well-designed distributed peer-to-peer network incentivize the smooth operation of a full-featured EHR system.

Technological advances in wearable and implanted medical devices are enabling wireless body area networks to alter the current landscape of medical and healthcare applications. These systems have the potential to significantly improve real time patient monitoring, provide accurate diagnosis and deliver faster treatment. In spite of their growth, securing the sensitive medical and patient data relayed in these networks to protect patients' privacy and safety still remains an open challenge. The resource constraints of wireless medical sensors limit the adoption of traditional security measures in this domain. In this work, we propose a distributed mobile agent based intrusion detection system to secure these networks. Specifically, our autonomous mobile agents use machine learning algorithms to perform local and network level anomaly detection to detect various security attacks targeted on healthcare systems. Simulation results show that our system performs efficiently with high detection accuracy and low energy consumption.