Biblio

This research covers the problem related to user behavior and its relationship with the protection of computer assets in terms of confidentiality, integrity, and availability. The main objective was to evaluate the relationship between the dimensions of awareness, compliance and appropriation of the information security culture and the asset protection variable, the ISCA diagnostic instrument was applied, and social engineering techniques were incorporated for this process. The results show the levels of awareness, compliance and appropriation of the university that was considered as a case study, these oscillate between the second and third level of four levels. Similarly, the performance regarding asset protection ranges from low to medium. It was concluded that there is a significant relationship between the variables of the investigation, verifying that of the total types of incidents registered in the study case, approximately 69% are associated with human behavior. As a contribution, an information security culture model was formulated whose main characteristic is a complementary diagnostic process between surveys and social engineering techniques, the model also includes the information security management system, risk management and security incident handling as part of the information security culture ecosystem in an enterprise.

Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.

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.

Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.

Preserving medical data is of utmost importance to stake holders. There are not many laws in India about preservation, usability of patient records. When data is transmitted across the globe there are chances of data getting tampered intentionally or accidentally. Tampered data loses its authenticity for diagnostic purpose, research and various other reasons. This paper proposes an authenticity based ECDSA algorithm by signature verification to identify the tampering of medical image files and alerts by the rules of authenticity. The algorithm can be used by researchers, doctors or any other educated person in order to maintain the authenticity of the record. Presently it is applied on medical related image files like DICOM. However, it can support any other medical related image files and still preserve the authenticity.

With the development of big data and machine learning techniques, medical data sharing for the use of disease diagnosis has received considerable attention. Blockchain, as an emerging technology, has been widely used to resolve the efficiency and security issues in medical data sharing. However, the existing studies on blockchain-based medical data sharing have rarely concerned about the reasonable incentive mechanism. In this paper, we propose a cooperation model where medical data is shared via blockchain. We derive the topological relationships among the participants consisting of data owners, miners and third parties, and gradually develop the computational process of Shapley value revenue distribution. Specifically, we explore the revenue distribution under different consensuses of blockchain. Finally, we demonstrate the incentive effect and rationality of the proposed solution by analyzing the revenue distribution.

The performance of the trust evaluation strategy depends on the accuracy and rationality of the trust evaluation weight system. Trust is a difficult to accurate measurement and quantitative cognition in the heart, the trust of the traditional evaluation method has a strong subjectivity and fuzziness and uncertainty. This paper uses the AHP method to determine the trust evaluation index weight, and combined with grey system theory to build trust gray evaluation model. The use of gray assessment based on the whitening weight function in the evaluation process reduces the impact of the problem that the evaluation result of the trust evaluation is not easy to accurately quantify when the decision fuzzy and the operating mechanism are uncertain.

With all the exciting benefits of IoT in healthcare - from mobile applications to wearable and implantable health gadgets-it becomes prominent to ensure that patients, their medical data and the interactions to and from their medical devices are safe and secure. The security and privacy is being breached when the mobile applications are mishandled or tampered by the hackers by performing reverse engineering on the application leading to catastrophic consequences. To combat against these vulnerabilities, there is need to create an awareness of the potential risks of these devices and effective strategies are needed to be implemented to achieve a level of security defense. In this paper, the benefits of healthcare IoT system and the possible vulnerabilities that may result are presented. Also, we propose to develop solutions against these vulnerabilities by protecting mobile applications using obfuscation and return oriented programming techniques. These techniques convert an application into a form which makes difficult for an adversary to interpret or alter the code for illegitimate purpose. The mobile applications use keys to control communication with the implantable medical devices, which need to be protected as they are the critical component for securing communications. Therefore, we also propose access control schemes using white box encryption to make the keys undiscoverable to hackers.

In this paper, new image encryption based on singular value decomposition (SVD), fractional discrete cosine transform (FrDCT) and the chaotic system is proposed for the security of medical image. Reliability, vitality, and efficacy of medical image encryption are strengthened by it. The proposed method discusses the benefits of FrDCT over fractional Fourier transform. The key sensitivity of the proposed algorithm for different medical images inspires us to make a platform for other researchers. Theoretical and statistical tests are carried out demonstrating the high-level security of the proposed algorithm.

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.

Rapid advancement in wearable technology has unlocked a tremendous potential of its applications in the medical domain. Among the challenges in making the technology more useful for medical purposes is the lack of confidence in the data thus generated and communicated. Incentives have led to attacks on such systems. We propose a novel lightweight scheme to securely log the data from bodyworn sensing devices by utilizing neighboring devices as witnesses who store the fingerprints of data in Bloom filters to be later used for forensics. Medical data from each sensor is stored at various locations of the system in chronological epoch-level blocks chained together, similar to the blockchain. Besides secure logging, the scheme offers to secure other contextual information such as localization and timestamping. We prove the effectiveness of the scheme through experimental results. We define performance parameters of our scheme and quantify their cost benefit trade-offs through simulation.