1,720,961 research outputs found
Framework for user awareness and acceptance of smart card and fingerprint-based access control to medical information systems in South Africa
Full text linkAbstract: A concern in the South African healthcare sector has been how to better secure medical information from unauthorised access, which may be through intentional or unintentional behaviour of users. The lack of user awareness and user acceptance of user access on medical information systems, particularly in the healthcare sector of South Africa, is a challenging issue in the area of the security of information. The challenge results in breaches to information stored in medical information systems, fraud, and medical identity theft. This study focuses on awareness and acceptance of secure access control using fingerprint and smart-card authentication to access medical systems by raising user awareness and improving acceptance of the technology. The security of patient health records stored in the medical information systems plays a substantial role in the hospital organisation of South Africa. Patients’ medical records are classified as sensitive by the National Department of Health in South Africa, and such information needs a high level of confidentiality and security from access by unauthorised individuals, be it internal or external access. Since the use of electronic medical records is progressing quickly, the need to train and educate users increases as a result. This training is necessitated to ensure that users are aware of the risks and vulnerabilities to information, and that they accept the responsibility to securely access information and protect the confidentiality and privacy of this information. Controlling access in environments, such as the healthcare sector of South Africa, where a large amount of confidential medical information is kept, is important for all citizens. A better solution is required to protect the privacy of electronic medical records in the healthcare sector, since the lack of proper access-control mechanisms increases the risk of vulnerabilities to patient health information saved in medical information systems, such as fraud, corruption, and theft. The use of a fingerprint biometric system provides an advantage over password-based authenticators due to the unique characteristics which a user presents, thereby restricting unauthorised access. Smart cards are useful and effective for the protection against attacks due to their encryption technologies, flexibility in authenticating a user, and its requirement for a user to possess it. Smart cards, combined with biometrics for authentication to medical systems, are created by storing the biometric template on the smart card. The study followed a qualitative and interpretive research design to collect data using survey questions, and observation of participants. The participants include the Records Management Unit, Information Management Unit, Records Manager, and ICT Manager at the hospital. This study proposes a hospital access control-based framework for user awareness...M.Sc. (Informatics
A model for the automated detection of fraudulent healthcare claims using data mining methods
Full text linkAbstract : The menace of fraud today cannot be underestimated. The healthcare system put in place to facilitate rendering medical services as well as improving access to medical services has not been an exception to fraudulent activities. Traditional healthcare claims fraud detection methods no longer suffice due to the increased complexity in the medical billing process. Machine learning has become a very important technique in the computing world today. The abundance of computing power has aided the adoption of machine learning by different problem domains including healthcare claims fraud detection. The study explores the application of different machine learning methods in the process of detecting possible fraudulent healthcare claims fraud. We propose a data mining model that incorporates several knowledge discovery processes in the pipeline. The model makes use of the data from the Medicare payment data from the Centre for Medicare and Medicaid Services as well as data from the List of Excluded Individual or Entities (LEIE) database. The data was then passed through the data pre-processing and transformation stages to get the data to a desirable state. Once the data is in the desired state, we apply several machine learning methods to derive knowledge as well as classify the data into fraudulent and non-fraudulent claims. The results derived from the comprehensive benchmark used on the implemented version of the model, have shown that machine learning methods can be used to detect possible fraudulent healthcare claims. The models based on the Gradient Boosted Tree Classifier and Artificial Neural Network performed best while the Naïve Bayes model couldn’t classify the data. By applying the correct pre-processing method as well as data transformation methods to the Medicare data, along with the appropriate machine learning methods, the healthcare fraud detection system yields nominal results for identification of possible fraudulent claims in the medical billing process.M.Sc. (Computer Science
Face recognition-based authentication and monitoring in video telecommunication systems
Full text linkM.Sc. (Computer Science)A video conference is an interactive meeting between two or more locations, facilitated by simultaneous two-way video and audio transmissions. People in a video conference, also known as participants, join these video conferences for business and recreational purposes. In a typical video conference, we should properly identify and authenticate every participant in the video conference, if information discussed during the video conference is confidential. This prevents unauthorized and unwanted people from being part of the conference and exposing any confidential information during the video conference. Present existing video conferencing systems however, have problems in this area, resulting in some risks. These risks relate precisely to the lack of facilities to properly identify and authenticate participants, making it possible for unwanted/unauthorised participants to join the conference or masquerade as another participant. It is especially a problem, when facilitators or organisers are the only participants that know the authorised participants, or participants allowed in a video conference. In this dissertation, we review the risks that are present in video conferencing, and create a security system, (called BioVid) that mitigates the identification and authentication risks in video conferences. BioVid uses a Speeded-Up Robust Features or SURF-based face recognition approach, to identify and authenticate any participant in a video conference. BioVid continuously monitors the participants to check if masquerading has occurred and when it does detect an unauthorised participant, it informs the Service Provider. The Service Provider can then deal with the problem by either kicking the participant or asking the other participants to vote the unauthorised participant out of the video conference
Collective human biological signal-based identification and authentication in access control environments
Full text linkPh.D. (Computer Science)The introduction of new portable sensors that monitor physiological systems in the human body has allowed quality of life and medical diagnostic applications to be taken directly to the user, without the constraints of physical space or inconvenience. The potential of these sensors in the domain of authentication and identi cation is becoming more feasible each day and current research in these biometric systems show a great deal of promise. Novel biometric systems are being introduced that use biological signals (also known as biosignals) in the human body captured by these sensors (such as brain waves or heart rate) as the core unique attribute. The study builds on the proliferation of these sensors and proposes an interoperable model called CoBI, which allows individual or multi-factor authentication and identi cation to take place. The model provides a platform for any viable biosignal that can be used for the purposes of identi cation and authentication, by providing pluggable sensor and signal processing components. These components can then convert biosignals into a common format, a feature vector consisting of estimated autoregressive (AR) coe cients. Once they are in a common format they can then be merged together to form a consolidated feature vector using feature fusion. This consolidated feature vector can then be persisted during enrolment or passed further for matching using classi cation techniques, such as K-Nearest Neighbour. The results, from the comprehensive benchmark performed (called BAMBI) on an implemented version of the model (called CaNViS), have shown that biological signals that contain cardiac and neurological components (ie. from an electrocardiogram (ECG) and electroencephalogram (EEG), respectively) can be captured, processed, consolidated and classi ed using the CoBI model successfully. By utilising the correct AR model order during feature estimation for the cardiac and neurological components, along with the appropriate classi er for matching, the biometric system yields nominal results for authentication and identi cation in access control environments
A model for protecting personal information using Blockchain
Full text linkAbstract: Users have lost control and ownership of their personal information in Cyberspace. Personal information is scattered across many company databases in Cyberspace and introduces many security risks, as well as a central point of attack that affects any user’s accounts containing personal information. Based on the information gathered on data breaches, the purpose of the research presented within this dissertation is to explore alternative methods that can be applied in Cyberspace. This will allow for the secured ownership and control of personal information. These methods will explore the mitigation of risks to personal information and improve the security of personal information in Cyberspace. By investigating the best methods to own and control personal information, some factors needed to be considered. Personal information would need to be transferable with users or parties in an environment which provides security, integrity, transparency, control, and interaction. In this dissertation, we will develop a model that will address the abovementioned points. The model is called the “SUUS CHAIN” model. “SUUS” means to be independent [1]. The chain is taken from the Blockchain term to indicate and support the use of the Blockchain technologies in this model. To support the development of the SUUS CHAIN model, we have chosen Blockchain technology which utilizes Smart Contracts. The Blockchain technology will provide the needed environment to securely send personal information between two parties while holding the integrity of the message, providing a fully auditable trail, and allow for the development of Smart Contracts. Smart Contracts will allow us to program any rules and conditions set out between two or more parties, concerning their personal information. We would also be allowed to program an authorization mechanism for interacting with user personal information. The contribution of our SUUS CHAIN model would be to allow users to own their personal information, as well as control how personal information is handled in Cyberspace. In doing so, we will also contribute to the improvement of securely transacting and sending personal information across Cyberspace. From the information and results accumulated throughout this dissertation, we have provided a working prototype demonstration of the SUUS CHAIN model. We have proven that the problem statement can be solved, and the objectives are met. Our SUUS CHAIN prototype demonstration, as well as the literature results provided, proves the security, control, and ownership of personal information can be accomplished.M.Sc. (Computer Science
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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