Real-time electrocardiogram streams for continuous authentication

Cámara, Carmen; Peris-López, Pedro; González-Manzano, Lorena; Tapiador, Juan E.

doi:10.1016/j.asoc.2017.07.032

Cited by 38 publications

(22 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the most important examples is the use of ECG information in real time. The studies shown in [ 50 , 51 ] demonstrate the feasibility of non-contact ECG measurements for CA purposes using different classifiers (SVM, KNN, etc.) and information (statistical, morphological, or wavelet properties).…”

Section: Continuous Authenticationmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

View full text Add to dashboard Cite

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

show abstract

Section: Continuous Authenticationmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Estes trabalhos consideraram a hipótese de que o ECG possui informações suficientes para aplicabilidade no reconhecimento humano. Especificamente, [Odinaka et al 2012] pesquisou cinquenta estudos dedicadosà identificação humana, onde 66% dos artigos pesquisados empregaram características não-fiduciais, 26% aplicaram características fiduciais, e 8% [ Camara et al 2018] consideraram um cenário de uma torre de controle de tráfego aéreo. Nesse conceito, os pesquisadores assumiram que os controladores exigiam monitoramento permanente para evitar incidentes de segurança, como um intruso tentando usar o sistema de controle, um controlador assumindo a posição de um colega e a alta variação da frequência cardíaca do controlador devido a uma situação estressante.…”

Section: Features Consideradas Para Sinais Ecgunclassified

“…Nesse sentido, o módulo de extração de recursos analisa a janela de ECG e a envia para uma Transformada Walsh-Hadamard (HT). [Camara et al 2018] consideraram o HT menos computacionalmente complexo que as transformadas de Fourier ou Wavelet. O modelo de aprendizado de máquina implementou um algoritmo k-NN, usando o conjunto de dados MIT-BIH Normal Sinus Rhythm [Goldberger et al 2000a].…”

Section: Features Consideradas Para Sinais Ecgunclassified

“…Além disso, a identificação correta de um número significativo de indivíduosé uma condição sine qua non para a avaliação do modelo. [Camara et al 2018] consideraram os avanços da Internet das Coisas (IoT) e dos dispositivos médicos implantáveis da próxima geração. Nesse sentido, os autores observaram que a mineração de fluxo de dadosé umaárea de pesquisa promissora para lidar com a autenticação contínua.…”

Section: Features Consideradas Para Sinais Ecgunclassified

See 1 more Smart Citation

Autenticação usando Sinais Biométricos: Fundamentos, Aplicações e Desafios

Cerqueira¹,

Resque²,

Medeiros³

et al. 2019

Jornada De Atualização Em Informática 2019

View full text Add to dashboard Cite

Our systems and data (e.g., websites, smartphones, safes, cars, banks, airports) are protected by traditional authentication methods. However, the growing concern about information security and the deployment of smart cities are demanding efforts to find solutions that prevent theft, loss, unauthorized copy, or the forgery of keys, tokens, and passwords. The Internet of Things (IoT) enabled a large increase of personal data collected and published in the Internet. In this context, biometric authentication has earned more and more place as a security solution because they demand, in general, physical presence, vitality, and they are hard to falsify. Among the biometric signals used in academic or commercial products, we mention: iris, face, the palm of hand, fingerprints, walking, voice, electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). This chapter brings the state-of-the-art about the use of several biometric signals in an authentication system, contextualizing the applications already developed and the challenges they faced when coexisting with Smart Cities and the Internet of Things. Each type of biometric signal has its own challenges for data acquisition, cost, feature selection, and method to implement the classification. In addition, this chapter presents a review of the machine learning techniques used in biometric systems. The proper choice of the identification technique and classification directly influences results, costs and also the required amount of input data, and the quality of the captured data.

show abstract

“…Biometrics solutions based on ECG [8,9] or EEG signals [10,11] have been proposed for authentication purposes. Some authors have even studied its feasibility (ECG [12,13] or EEG [14,15]) in the context of continuous authentication—the verifier validates the credentials at regular intervals, ideally at every instant. The key distribution problem between two devices (e.g., two ECG sensors [16]) has also attracted the attention of some researchers.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

Cámara

Martín

Peris-López

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Today, medical equipment or general-purpose devices such as smart-watches or smart-textiles can acquire a person’s vital signs. Regardless of the type of device and its purpose, they are all equipped with one or more sensors and often have wireless connectivity. Due to the transmission of sensitive data through the insecure radio channel and the need to ensure exclusive access to authorised entities, security mechanisms and cryptographic primitives must be incorporated onboard these devices. Random number generators are one such necessary cryptographic primitive. Motivated by this, we propose a True Random Number Generator (TRNG) that makes use of the GSR signal measured by a sensor on the body. After an exhaustive analysis of both the entropy source and the randomness of the output, we can conclude that the output generated by the proposed TRNG behaves as that produced by a random variable. Besides, and in comparison with the previous proposals, the performance offered is much higher than that of the earlier works.

show abstract

Real-time electrocardiogram streams for continuous authentication

Cited by 38 publications

References 30 publications

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Autenticação usando Sinais Biométricos: Fundamentos, Aplicações e Desafios

Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

Contact Info

Product

Resources

About