Blockchain Driven Access Control Mechanisms, Models and Frameworks: A Systematic Literature Review

Access control or authorization is referred to as the confinement of specific actions of an entity, thereby allowing them to be performed as per certain rules. Blockchain-driven access control mechanisms gained considerable attention directly after applications beyond the premise of cryptocurrency were found. However, there are no systematic efforts to analyze existing empirical evidence. To this end, we aim to synthesize litera- ture to understand the state-of-the-art blockchain driven access control mechanisms with respect to underlying platforms, utilized blockchain properties, nature of the mod- els and associated testbeds and tools. We conducted the review in a systematic way. Meta analysis and thematic synthesis were performed on the findings from relevant primary studies, in order to answer the framed research questions in perspective. We identified 76 relevant primary studies that passed the quality assessment.  The problems targeted by relevant studies were single point of failure, security, and privacy, etc. The meta-analysis of the primary studies suggests the use of different blockchain platforms along with several application domains where different blockchain proprieties were utilized. In this paper, we present a systematic literature review of blockchain driven access control systems. In hindsight, we present a taxonomy of blockchain-driven access control systems to better understand the immense implications of this field spanning various application domain

Cleansing indoor RFID tracking data

The Radio-Frequency Identification (RFID) technology has been increasingly deployed in indoor environments for object tracking and monitoring. However, the uncertain characteristics of RFID data, including noise and incompleteness hinder RFID data querying and analysis at higher levels. Hence, it is of paramount importance to cleanse the RFID data for such applications. This paper introduces our comprehensive research on cleansing RFID data in indoor settings. We focus on two inherent errors in such RFID data: false positives (unexpected cross readings) and false negatives (missing readings). In our proposed graph model based approach , we design a probabilistic distance-aware graph to represent the indoor topology, the deployment of RFID readers and their sensing parameters. We also augment the graph with transition probabilities that capture how likely objects move from one RFID reader to another. Based on the proposed graph, we design cleansing algorithms to reduce false positives and recover false negatives. In the learning-based approach , we propose an Indoor RFID Multi-variate Hidden Markov Model (IR-MHMM) to capture the uncertainties of indoor RFID data as well as the correlation of moving object locations and object's RFID readings. We solely use raw RFID data for the learning of the IR-MHMM parameters. Using the resulting IR-MHMM, the learning-based approach is able to deliver cleansing performance comparable to and even better than that of the graph model based approach, although the former requires much less prior knowledge than the latter. </jats:p

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality