2,396 research outputs found
Integration of biometrics and steganography: A comprehensive review
The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards
Temporary Access to Medical Records in Emergency Situations
Access to patients Electronic Health Records (EHR) is a daily operation in mainstream healthcare. However, having access to EHR in emergencies while is vitally important to save patients’ life, it could potentially lead to security breaches and violating patients’ privacy. In this regards, getting access to patients’ medical records in emergency situations is one of the issues that emergency responder teams are facing. This access can be temporary until patients reach hospitals or healthcare centers. In this paper, we aim to explore different technology-based solutions to give responders temporary access to patients\u27 medical records in emergency situations. The core of this study is patients and responders authentication methods that can save precious emergency time and protect the privacy and confidentiality of patients data to the utmost. We also have explored control access mechanism and security audits to increase the security of the procedure and patient privacy
IOT based Security System for Auto Identifying Unlawful Activities using Biometric and Aadhar Card
In today’s era, where thefts are consecutively increasing, especially in banks, jewelry shops, stores, ATMs, etc, there is a need to either develop a new system or to improve the existing system, due to which the security in these areas can be enhanced. However, the traditional methods (CCTV cameras, alarm buttons) to handle the security issues in these areas are still available, but they have lots of limitations and drawbacks. So, in order to handle the security issues, this paper describes how the biometric and IoT (Internet of Things) techniques can greatly improve the existing traditional security system. Our proposed system uses biometric authentication using the fingerprint and iris pattern with the strength of IoT sensors, microcontroller and UIDAI aadhar server to enhance the security model and to cut the need of keeping extra employees in monitoring the security system
Development of Fingerprint Biometric Attendance System for Non-Academic Staff in a Tertiary Institution
Institutions, companies and organisations where security and net productivity is vital, access to certain areas must be controlled and monitored through an automated system of attendance. Managing people is a difficult task for most of the organizations and maintaining the attendance record is an important factor in people management. When considering the academic institute, taking the attendance of non-academic staff on daily basis and maintaining the records is a major task. Manually taking attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason, an efficient system is proposed in this paper to solve the problem of manual attendance. This system takes attendance electronically with the help of a fingerprint recognition system, and all the records are saved for subsequent operations. Staff biometric attendance system employs an automated system to calculate attendance of staff in an organization and do further calculations of monthly attendance summary in order to reduce human errors during calculations. In essence, the proposed system can be employed in curbing the problems of lateness, buddy punching and truancy in any institution, organization or establishment. The proposed system will also improve the productivity of any organization if properly implemented. Keywords: Institution, Attendance, Biometric, Fingerprin
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Biometric encryption system for increased security
Security is very important in present day life. In this highly-interconnected world, most of our daily activities are computer based, and the data transactions are protected by passwords. These passwords identify various entities such as bank accounts, mobile phones, etc. People might reuse the same password, or passwords related to an individual that can lead to attacks. Indeed, remembering several passwords can become a tedious task. Biometrics is a science that measures an individual’s physical characteristics in a unique way. Thus, biometrics serves as a method to replace the cumbersome use of complex passwords. Our research uses the features of biometrics to efficiently implement a biometric encryption system with a high level of security
Incorporating Zero-Knowledge Succinct Non-interactive Argument of Knowledge for Blockchain-based Identity Management with off-chain computations
In today's world, secure and efficient biometric authentication is of keen
importance. Traditional authentication methods are no longer considered
reliable due to their susceptibility to cyber-attacks. Biometric
authentication, particularly fingerprint authentication, has emerged as a
promising alternative, but it raises concerns about the storage and use of
biometric data, as well as centralized storage, which could make it vulnerable
to cyber-attacks. In this paper, a novel blockchain-based fingerprint
authentication system is proposed that integrates zk-SNARKs, which are
zero-knowledge proofs that enable secure and efficient authentication without
revealing sensitive biometric information. A KNN-based approach on the FVC2002,
FVC2004 and FVC2006 datasets is used to generate a cancelable template for
secure, faster, and robust biometric registration and authentication which is
stored using the Interplanetary File System. The proposed approach provides an
average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and
FVC2006 datasets respectively for fingerprint authentication. Incorporation of
zk-SNARK facilitates smaller proof size. Overall, the proposed method has the
potential to provide a secure and efficient solution for blockchain-based
identity management
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