Automatic Voting Machine – An Advanced Model for Secured Biometrics Based Voting System

India, which is now considered as the world’s largest democracy has been praised in the whole world for its democratic principles of “Sovereign, Socialist, Secular, Democratic, Republic”. It is now the second largest populous country in the world. Although the country has a rich technical and scientific infrastructure yet the voting and election procedures do not reflect it. In this paper we are going to propose a concept or idea about how the voting processes and equipments can be designed for a “free, fair and secure” polling in the upcoming days. Researchers have proved various problems related to the present voting technique in India which is through Electronic Voting Machines (EVM) [2]. Our proposed model of the equipment for voting will overcome those problems making elections & voting fair, convenient and reliable to every citizen. This proposed model will assure complete transparency and will definitely gain the trust and integrity of the voters. Biometric authentication and identity proof are given much priority while proposing the concept since biometric authentication is a type of system that relies on the unique biological characteristics (such as finger prints, retina scan, etc.) of individuals to verify identity for secure access to electronic systems. DOI: 10.17762/ijritcc2321-8169.15074

Novel active sweat pores based liveness detection techniques for fingerprint biometrics

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.This study is funded by the University of Sindh, Jamshoro, Pakistan and the Higher Education Commission of Pakistan

Detecção de vivacidade de impressões digitais baseada em software

Orientador: Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Com o uso crescente de sistemas de autenticação por biometria nos últimos anos, a detecção de impressões digitais falsas tem se tornado cada vez mais importante. Neste trabalho, nós implementamos e comparamos várias técnicas baseadas em software para detecção de vivacidade de impressões digitais. Utilizamos como extratores de características as redes convolucionais, que foram usadas pela primeira vez nesta área, e Local Binary Patterns (LBP). As técnicas foram usadas em conjunto com redução de dimensionalidade através da Análise de Componentes Principais (PCA) e um classificador Support Vector Machine (SVM). O aumento artificial de dados foi usado de forma bem sucedida para melhorar o desempenho do classificador. Testamos uma variedade de operações de pré-processamento, tais como filtragem em frequência, equalização de contraste e filtragem da região de interesse. Graças aos computadores de alto desempenho disponíveis como serviços em nuvem, foi possível realizar uma busca extensa e automática para encontrar a melhor combinação de operações de pré-processamento, arquiteturas e hiper-parâmetros. Os experimentos foram realizados nos conjuntos de dados usados nas competições Liveness Detection nos anos de 2009, 2011 e 2013, que juntos somam quase 50.000 imagens de impressões digitais falsas e verdadeiras. Nosso melhor método atinge uma taxa média de amostras classificadas corretamente de 95,2%, o que representa uma melhora de 59% na taxa de erro quando comparado com os melhores resultados publicados anteriormenteAbstract: With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implemented and compared various techniques for software-based fingerprint liveness detection. We use as feature extractors Convolutional Networks with random weights, which are applied for the first time for this task, and Local Binary Patterns. The techniques were used in conjunction with dimensionality reduction through Principal Component Analysis (PCA) and a Support Vector Machine (SVM) classifier. Dataset Augmentation was successfully used to increase classifier¿s performance. We tested a variety of preprocessing operations such as frequency filtering, contrast equalization, and region of interest filtering. An automatic and extensive search for the best combination of preprocessing operations, architectures and hyper-parameters was made, thanks to the fast computers available as cloud services. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013 that comprise almost 50,000 real and fake fingerprints¿ images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 59% in test error when compared with the best previously published resultsMestradoEnergia EletricaMestre em Engenharia Elétric

Who Wears Me? Bioimpedance as a Passive Biometric

Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable – they require ıt active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost). \par We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person\u27s body to recognize their identity. More specifically, the sensor uses ıt bioimpedance – a measure of how the body\u27s tissues oppose a tiny applied alternating current – and learns how a person\u27s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time

Vulnerabilities and attack protection in security systems based on biometric recognition

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 200

Securing health monitoring via body-centric time-frequency signature authorization

Identity-based attacks serve as the basis of an intruder’s attempt to launch security infringements in mobile health monitoring scenarios. Wireless channel perturbations due to the presence of human body are a relative phenomenon depending heavily on the subject’s dielectric properties. A new Body-Centric Signature Authorization (B-CSAI) approach based on time-frequency domain characteristics was proposed. This method utilizes multiple millimeter wave bands of 27-28 GHz, 29-30 GHz, and 31-32 GHz, thereby enhancing the security in body-centric communications exploiting benefits of subject specific channel signature. The proposed bornprint method is based on the intrinsic identity related time-frequency domain information, which generated by the user’s natural hand motion signature and resulting creeping waves and space waves. It can meet the unconditional keyless authorization requirements. A detailed measurement campaign considering radiation efficiency (η = -25.8, -24.7, -26.4), pathloss exponent, and shadowing factor in three millimeter wave bands, using six human subjects confirm the usability and efficiency of the proposed approach. This also shows that there is a wide space for realizing security from physical mechanisms

Usable Security for Wireless Body-Area Networks

We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors

A Blockchain System for Mobile Health Applications and Services

Com o aparecimento das tecnologias blockchain, o crescimento e adaptação de características criptográficas levaram à exploração de novos usos em novas áreas, como a computação móvel para a saúde (m-Health). Atualmente, estas tecnologias são implementadas primáriamente como mecanismos para manter os registos de saúde eletrónicos seguros. No entanto, novos estudos têm provado que estas apresentam-se como uma ferramenta poderosa para promover o controlo da informação de saúde pelos próprios pacientes e possibilita a existência de um historial médico sem alterações errôneas, para além da responsabilização dos profissionais de saúde. Nos últimos anos, verificou-se um rápido crescimento da área da m-Health, sustentada numa arquitetura orientada a serviços, levando a que a adaptação de mecanismos de blockchain em aplicações de saúde gerasse a possibilidade da existência de um serviço mais descentralizado, pessoal e disponível. A ideia de adaptar tecnologia blockchain na área da prestação de serviços de saúde apresenta inicialmente alguns pontos críticos, como por exemplo como é que é assegurada a segurança e a privacidade da informação de saúde guardada na blockchain. Normalmente, num sistema completamente descentralizado, a informação tem de estar completamente disponível a atores externos e tem de ser guardada de forma distribuída. Embora o armazenamento da informação de forma distribuída não apresente dificuldades, a particulariedade do tipo de informação que é guardada na blockchain e de que maneira esta é mantida privada e segura são questões problemáticas já conhecidas. Um breve estudo desta tecnologia é suficiente para concluir que não é adequado um registo médico de um paciente ser guardado na blockchain, uma vez que, devido ao tamanho do registo, iria gerar problemas de escalabilidade com o aumento do número de pacientes. Perante esta situação, o desempenho da blockchain iria diminuir e seria necessária uma quantidade demasiado elevada de poder computacional para a realização de tarefas básicas, gerando ainda um aumento nos requisitos de armazenamento e de transmissão em rede. Embora a blockchain não tenha capacidade para guardar a informação completa de um paciente, as suas características permitem que seja utilizada para guardar outros dados relacionados com a privacidade da informação da saúde. Deste modo, é precisamente no registo e controlo de acesso à informação de saúde que a tecnologia blockchain promete inovar. Ao registar todos os acessos à informação de saúde de um paciente, é possível criar um registo com a identificação e a autentificação de todos os utilizadores do sistema que requereram o acesso a determinada informação de saúde. Portanto, um registo de acesso consegue ser criado com uma pequena quantidade de informação, como um timestamp, com a identificação do utilizador que está a aceder aos dados e com a identificação do utilizador cujos dados estão a ser acedidos. Uma das grandes vantagens de registar a informação dos acessos numa blockchain é o facto de os registos serem distribuídos por várias localizações, sendo estas imutáveis e tolerantes a falhas e públicos. Deste modo, verifica-se que os problemas de escalabilidade, associados ao tamanho reduzido do registo, que surgem ao guardar informação na blockchain discutidos previamente conseguem ter um impacto mais reduzido. Contudo, apesar das vantagens desta tecnologia, alguns dos aspetos da sua integração desta em m-Health não são compatíveis com a natureza da informação de saúde de um paciente. Para acomodar tecnologia blockchain na área da saúde, é necessário que o sistema seja construído com várias restrições em mente. Uma destas restrições é o facto de que a informação presente na blockchain é normalmente pública, o que entra em conflito com o direito à privacidade dos pacientes e leva à necessidade de encriptar a informação. Outra restrição é o facto de como identificar um utilizador num registo de acesso, uma vez que normalmente a informação dos utilizadores é anónima. O objetivo desta dissertação é estudar como a tecnologia blockchain consegue ser conjugada com a informação de saúde recolhida ou processada por aplicações móveis. Com a finalidade de alcançar esse objetivo, foi desenvolvido um protótipo de uma solução baseada em blockchain para controlar acesso à informação de saúde. Este protótipo para além de oferecer uma segurança melhorada da informação, devido à implementação de mecanismos de criptografia, oferece um historial médico imutável ao armazenar informação de eventos de saúde numa blockchain. A esta construção foi ainda adicionado um sistema de armazenamento de dados anónimos baseado numa arquitetura de data lake. Posteriormente, este protótipo foi integrado num ambiente de teste, que consistiu em várias aplicações móveis, com o objetivo de testar detalhadamente a viabilidade e desempenho de propostas similares.With the advent of blockchain, the growth and adaptation of cryptographic features and capabilities were quickly extended to new and under-explored areas, such as healthcare. Currently, blockchain is being implemented mainly as a mechanism to secure Electronic Health Record (EHR)s. However, new studies have shown that this technology can be a powerful tool in empowering patients to control their own health data, as well as for enabling a fool-proof health data history and establishing medical responsibility. With the advent of mobile health (m-Health) sustained on service-oriented architectures, the adaptation of blockchain mechanisms into m-Health applications creates the possibility for a more decentralized and available healthcare service. The idea of adapting blockchain technology into healthcare initially presents several critical points where special consideration is required, such as how privacy and security of healthcare information can be assured if information is stored into a blockchain. Usually, for a completely decentralized system, the information has to be available to everyone and is to be stored in a distributed manner. While the storage of the information being distributed is not difficult, what kind of information should be stored into the blockchain as well as how this information can be kept private and secure present issues. A brief study of blockchain technology is enough to conclude that a full patient record is not fit to be stored into a blockchain, because the size of the record would create scalability problems as the number of patient records increases. This diminishes the performance of the blockchain to where the amount of computational power needed to perform basic tasks would rise considerably, as well as the storage and network requirements needed to permanently store the information and to replicate the information throughout the whole network, respectively. However, other uses for blockchain technology arise once the nature of the health information is analyzed thoroughly. Because of the highly personal and private aspect of health information belonging to a patient, the security of how that information is stored, transmitted and accessed becomes a main focus of health systems. It is precisely in access recording and management of healthcare information that blockchain shows promise in implementation. By recording all accesses to a the health information of a patient, it is possible to create a log of every user in a system that has had access to some information. By having a system that identifies and authenticates all users, every access to health data can be recorded as having been done by an identified user. An access record can be made with a small amount of information, such as a timestamp, an accessing user identifier and an identifier of the user whose data is being accessed. Because an access record can be accomplished with only this amount of information, the scalability issues that where discussed earlier regarding storing information into a blockchain can be mitigated. In terms of advantages, recording access information into a blockchain results in the access records being distributed across several locations, immutable, fault-tolerant and public. However, some aspects of the integration of blockchain into healthcare result in incompatibilities of the nature of health information and of blockchain. To accommodate health information and blockchain, the surrounding system must be constructed with several limitations in mind. One of which is the public nature of blockchain not being in line with the private nature of health information and therefore the information must be encrypted, or how a user can be identified in an access record if usually information in a blockchain is anonymous. This work proposes a system that successfully integrates blockchain into an m-Health testbed, outlining how both areas have evolved and their main challenges. The proposed system offers enhanced information security both in transmission, storage and access, by integrating several cryptographic mechanisms. Furthermore it is integrated with a blockchain access system and a high volume anonymous information storage mechanism based on a data lake database architecture. This system is integrated into a testbed that allows for a more detailed discussion on viability and performance of similar concepts

An industrial revolution for fingerprint science? The impact of cognition and human factors on fingerprint examiners: implications for the use of fingerprint examiner expertise and administration within law enforcement.

Fingerprint analysis has been a cornerstone of law enforcement investigation for well over 100 years (Block 1970, Duncan 1942, Holt 1936, Beavan 2001, Sengoopta 2003, Cooke 1932 and Charlton et al 2007). Fingerprint evidence has rarely been challenged by either the public or the judicial system. However, the people entrusted to perform the analysis of fingerprints are increasingly being seen as the weak link in the chain of evidence by some commentators (Schneier 2003). Factors that affect the mind and its cognitive processes such as context and emotional state have an impact on decision making associated with a multitude of human endeavours including the medical and the military professions. This has been largely ignored by the forensic domain at large and by fingerprint examination specialists in particular. Errors in the analysis of fingerprint evidence in high profile cases such as Shirley McKie and Brandon Mayfield (Thompson et al 2005, Zeelenburg 2008, McKie 2007) have resulted latterly in legal counsel, media and the public asking whether fingerprint examination is valid and safe forensic science (Saks et al 2005). Psychologists have highlighted potential weaknesses in the policing domain and continue to make recommendations for improvement in order to minimise the risk of wrongful convictions (Adler 2004). This thesis suggests that fingerprint examiners are not only emotionally driven and motivated to achieve results, but also that the motivations of examiners (Kruglanski et al 1983, Kruglanski et al 1987, Kruglanski 1989) exert leverage upon decision making thresholds. The consistency of fingerprint examiner observations during the analysis of fingerprints is also observed, in addition to the performance of fingerprint examiners during their interaction with colleagues and technology. This thesis provides evidence of systemic practitioner inconsistency based on process and procedural weaknesses based on the cognitive realities that pervade the very nature of the work the human fingerprint examiners carry out. Through understanding of the human factors that impact upon the fingerprint examiner domain it has been possible in this thesis to offer insight and intervention that can mitigate against error and methodological breakdown of fingerprint analysis in the future, as well as to facilitate the design and implemetation of effective and robust recruitment, selection and training environments. In addition this thesis provides recommendations to facilitate the provision of fit for purpose technologies that are able to provide best practice for examiners and to satisfy public confidence in not only fingerprint examination but also other forensic and more generic expert policing domains