Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

Hybrid Security Framework for Activity Based Authentication using RSA & Genetic Algorithm

In the current information age, security has achieved a tremendous importance in e-commerce applications involving financial transactions. Non-repudiation, data integrity, data confidentiality and authenticity, have become an integral part of information security. There is a tremendous risk involved in the communication of a plain text over Internet. Cryptography offers a solution for this type of risk which is referred to as a technique of encrypting and decrypting messages in such a way that they cannot be interpreted by anybody with the exception of a sender and an intended recipient. In majority of the e-commerce based applications where security is considered to be of prime importance, a single encryption algorithm is adopted for encrypting a password and the authentication information is stored on a single database server which becomes open to risks against different computer hacks. A novel solution for this problem is to generate an individual’s personal and dynamic activities which will be hard for the attackers to guess. Further, this can be combined with distributed technology where the authentication information is distributed over geographically separated multiple servers. In this paper authors have generated an activity based distributed 3D password incorporating various activities where the authentication information is distributed over geographically separated multiple authentication servers. The key pair is generated using RSA algorithm which is encrypted using single-point cross over and mutation of bits at the extreme position. This further adds another level of security and renders the key unbreakable by an unintended user. The configuration information pertaining to the distributed environment is stored in XML file which is parsed using Microsoft's XML Parser and the activity related information is stored in different servers which is encrypted using RSA algorithm. The technique employed combines RSA algorithm with Genetic Algorithm to offer a robust hybrid security framework in a distributed environment which is difficult to guess for an unintended user

A Threat Taxonomy for mHealth Privacy

Networked mobile devices have great potential to enable individuals (and their physicians) to better monitor their health and to manage medical conditions. In this paper, we examine the privacy-related threats to these so-called \emphmHealth\/ technologies. We develop a taxonomy of the privacy-related threats, and discuss some of the technologies that could support privacy-sensitive mHealth systems. We conclude with a brief summary of research challenges

ECG biometric recognition : permanence analysis of QRS signals for 24 hours continuous authentication

Recent studies regard the use of ECG signals for biometric recognition exploiting the possibility of these signals to be frequently recorded for long time periods without any explicit actions performed by the users during the acquisitions. This aspect makes ECG signals particularly suitable for continuous authentication applications. In this context, researches have proved that the QRS complex is the most stable component of the ECG signal. In this paper, we perform a preliminary study on the persistency of QRS signals for continuous authentication systems. A recognition method based on multiple leads is proposed, and used to evaluate the persistency of the QRS complex in 24 hours Holter signals. This time interval can be considered as adequate for many possible applications in continuous authentication scenarios. The analysis is performed on a significantly large public Holter dataset and aims to search accurate matching and enrollment strategies for continuous authentication systems. At the best our knowledge, the results presented in this paper are based on the biggest set of ECG signals used to design continuous authentication applications in the literature. Results suggest that the QRS complex is stable only for a relatively small time period, and the performance of the proposed recognition method starts decreasing after two hours

Towards fine-grained urban traffic knowledge extraction using mobile sensing

We introduce our vision for mining fine-grained urban traf-fic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring tra±c congestion, we show how mobile sensing can also reveal details such as intersection performance statis-tics that are useful for optimizing the timing of a tra±c sig-nal. Realizing such applications requires co-designing pri-vacy protection algorithms and novel tra±c modeling tech-niques so that the needs for privacy preserving and tra±c modeling can be simultaneously satisfied. We explore pri-vacy algorithms based on the virtual trip lines (VTL) con-cept to regulate where and when the mobile data should be collected. The tra±c modeling techniques feature an inte-gration of tra±c principles and learning/optimization tech-niques. The proposed methods are illustrated using two case studies for extracting tra±c knowledge for urban signalized intersection

A QR Code Based Zero-Watermarking Scheme for Authentication of Medical Images in Teleradiology Cloud

Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)—Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu’s invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks

Privacy-Preserving ECG Based Active Authentication (PPEA2) Scheme for Iot Devices

Internet of things (IoT) devices are becoming ubiquitous in, and even essential to, many aspects of day-to-day life, from fitness trackers, pacemakers, to industrial control systems. On a larger scale, live stream of sleep patterns data recorded via fitness tracker devices was utilized to quantify the effect of a seismic activity on sleep. While the benefits of IoT are undeniable, IoT ecosystem comes with its own set of system vulnerabilities that include malicious actors manipulating the flow of information to and from the IoT devices, which can lead to the capture of sensitive data and loss of data privacy. My thesis explores a Privacy-Preserving ECG based Active Authentication (PPEA2) scheme that is deployable on power-limited wearable systems to counter these vulnerabilities. Electrocardiogram (ECG) is a record of the electrical activity of the heart, and it has been shown to be unique for every person. This work leverages that idea to design a feature extraction followed by an authentication scheme based on the extracted features. The proposed scheme preserves the privacy of the extracted features by employing a light-weight secure computation approach based on secure weighted hamming distance computation from an oblivious transfer. It computes a joint set between two participating entities without revealing the keys to either of them

Heartbeats in the Wild: A Field Study Exploring ECG Biometrics in Everyday Life

This paper reports on an in-depth study of electrocardiogram (ECG) biometrics in everyday life. We collected ECG data from 20 people over a week, using a non-medical chest tracker. We evaluated user identification accuracy in several scenarios and observed equal error rates of 9.15% to 21.91%, heavily depending on 1) the number of days used for training, and 2) the number of heartbeats used per identification decision. We conclude that ECG biometrics can work in the wild but are less robust than expected based on the literature, highlighting that previous lab studies obtained highly optimistic results with regard to real life deployments. We explain this with noise due to changing body postures and states as well as interrupted measures. We conclude with implications for future research and the design of ECG biometrics systems for real world deployments, including critical reflections on privacy.Comment: 14 pages, 10 figures, CHI'2

Household ECG

Import 23/08/2017Tato diplomová práce se zabývá návrhem a konstrukcí přístroje měřícího signál EKG. Konstrukční řešení se zaměřuje na modularitu a minimální velikost celého řešení. Elektrokardiograf je snadno přenositelný a umožňuje bezdrátový přenos dat do počítače s vizualizací elektrokardiogramů v uživatelsky přívětivém programu vytvořeném ve vývojovém prostředí LabVIEW. Program umožňuje vizualizaci 12svodového EKG v reálném čase s dopočítanou tepovou frekvencí. Toto řešení elektrokardiografu je díky svým parametrům vhodné pro domácí nebo ambulantní použití.This diploma thesis deals with the design and construction of the ECG measuring signal device. The design solution focuses on modularity and minimal size of the solution. The electrocardiograph is easy to transfer and allows wireless data transfer to a computer with visualization of electrocardiograms in a user-friendly program created in the LabVIEW development enviroment. The program allows real-time visualization of 12 lead ECGs with a calculated heart rate. This electrocardiograph solution is suitable for home or outpatient use due to its parametrs.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn