Biometric systems use scanners to verify the identity of human beings by measuring the patterns of their behavioral or physiological characteristics. Some biometric systems are contactless and do not require direct touch to perform these measurements; others, such as fingerprint verification systems, require the user to make direct physical contact with the scanner for a specified duration for the biometric pattern of the user to be properly read and measured. This may increase the possibility of contamination with harmful microbial pathogens or of cross-contamination of food and water by subsequent users. Physical contact also increases the likelihood of inoculation of harmful microbial pathogens into the respiratory tract, thereby triggering infectious diseases. In this viewpoint, we establish the likelihood of infectious disease transmission through touch-based fingerprint biometric devices and discuss control measures to curb the spread of infectious diseases, including COVID-19.

Biometric Authentication Systems (BAS) have several security benefits over traditional password and token authentication including an inherent difficulty to copy, clone and share or distribute authentication credentials (biometric traits). Spoofing or presentation attack remains a major weakness of biometric systems and tackling it at the trait level is still challenging with several different approaches and methods applied in existing systems. In this paper, we focus on the well-known approach of Suspicious Presentation Detection (SPD) and present the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) that further mitigates spoofing or presentations attacks using randomization and combination of several different SPD detection techniques across three different modalities during trait capture. We discuss the detection of life using five distinct properties each from finger, face and eye modalities and present results from a simulation that highlights the improved security based on an impostor's inability to accurately predict the combination of trait liveness properties the system might prompt and test for during capture.

BackgroundIncreasing global commitment to Universal Health Coverage (UHC) in the past decade has triggered UHC-inspired reforms and investments to expand health service coverage in many Low- and Middle-Income Countries (LMICs). UHC aims to ensure that all people can access quality health services, safeguard them from public health risks and impoverishment from out-of-pocket payments for healthcare when household members are sickAimThis paper reviews the role of health insurance as a policy tool to address health financing as a contributory mechanism for accelerating the achievement of UHC in LMICs. We focus on Nigeria's legal framework for health insurance coverage for its whole population and the role of technology in facilitating enrollment to health insurance schemes.MethodsFrom May to July 2022, we adopted a cross-sectional case study design combining: (i) a literature review of the effects of UHC with (ii) document analysis of health insurance systems in Nigeria, and (iii) secondary analysis of health insurance datasets to understand experiences of deploying MedStrat, a locally-developed digital health insurance management system, and its features that support the administration of health insurance schemes in multiple states of Nigeria. We drew on contemporary technology adoption models to triangulate diverse data analyzed from literature and documents reviews and from health insurance datasets to identify: (i) enablers of adoption of digital insurance schemes, (ii) the contribution of digital technology to expanding access to health insurance, and (iii) further scalability of digital insurance intervention.ResultsPreliminary findings suggests that digital insurance management systems can help to increase the number of enrollees for insurance especially among poor households. Three contextual enablers of adoption of digital insurance schemes were a favourable policy environment, public-private-partnerships, and sustained stakeholder engagement and training.Discussion and conclusionKey elements for successful scaling of digital health insurance schemes across Nigeria and similar contexts include: (i) ease of use, (ii) existing digital infrastructure to support electronic insurance systems, and (iii) trust manifested via data encryption, maintaining audit trails for all data, and in-built fraud prevention processes. Our findings affirm that digital health technology can play a role in the attainment of UHC in LMICs.

Despite their advantages over password-based and token-based authentication, Biometric Authentication Systems (BAS) are not perfect. They are particularly vulnerable to spoofing, also called Suspicious Presentation (SP) attacks whereby an impostor presents a fake trait to the biometric scanner during verification. Spoofing has a critical impact on system security leading to a trust deficit on biometric systems with weak anti-spoofing mechanisms. Mitigating biometric spoofing is a possibility, hence several techniques have evolved in recent times including multi-biometrics, biometric cryptography and Liveness Detection (LD) -also called Suspicious Presentation Detection (SPD). Unfortunately, nearly all known LD techniques exhibit a fundamental set of flawsthey are mostly uni-modal, easily predictable by a well-equipped impostor, and can be circumvented by well-crafted SP attacks. This paper presents the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) framework, as an alternative approach that implements LD using multiple traits each acquired from separate modalities of the same subject combined in a randomized manner. The strength of the framework lays in the impostor's inability to accurately predict the exact set of randomized trait parameter combinations in advance of LD. The framework employs a 3D simulation of fifteen liveness parameters, composed of three each from finger, face and iris traits, based on random number generation. Simulation results obtained using 125 distinct randomized combinations show significant improvements in biometric authentication security with a system efficiency of 99.2%.

The challenges faced by organizations in effectively carrying out digital forensic operations have been identified to include poor capacity and defective Information and Communications Technology (ICT) policies, resulting in organizational cluelessness when high profile cyberattacks occur. Organizational unpreparedness in the face of cyberattacks leads to negative impacts including operational disruption, financial loss, reputation damage and crippling litigations. Unfortunately, the sophistication of contemporary cyberattacks makes their investigations even more difficult except where a methodical approach for forensic data collection and analysis is adopted. This paper proposes the Randomized Cyberattack Simulation Model (RCSM) as a checklist for assessing corporate preparedness to digital forensics and as a systematic approach towards enshrining organizational incident response capabilities. The paper also proposes the Baseline Data Classification Model (BDCM) as a pre-forensic data categorization model. Prudent application of both models can potentially mitigate the impacts of cyberattacks on operational sustainability and organizational survivability across all sectors.