We present our work on testing access control of large national e-health Internet portal which has millions of monthly visits. Our aim is twofold: (1) to improve testing by applying systematic and rigorous (semi-formal) approach and (2) to obtain holistic view of portal’s complex access control structure. Applying more rigorous approach facilitates reducing ambiguity while holistic picture aids on easier and often also faster comprehension of complex access control structure. We use set-theoretic approach for specifying access control. Then, from access control’s abstract set notations we derive a visual version in form of the access control tree. Nodes of the tree represent attributes that influence access while edges are values of those attributes. The leaf of the tree represents a scope which is a grouping of individual services. Access control tree presented in this paper has 15 scopes (leaves) which results in 105 pairs of abstract test scenarios. Complete version of the tree has 66 scopes that result in over 2000 pairs of abstract test scenarios. Abstract test scenarios are implemented into over 600 concrete and automated test cases. Manual execution test of one concrete test takes about five minutes while automated execution of all tests takes about one hour (thus achieving over 40 times speedup). These automated test cases run as a part of our CI/CD pipeline. Access control tree can also be used as a collaboration or learning tool, to get quicker familiarity with the solution.

We present lightweight model-based testing (MBT) of privacy and authorization concepts of national portal for electronic health services in Norway (which has over a million of visits per month). We have developed test models for creating and updating privacy levels and authorization categories using finite state machine. Our models emphasize not only positive but also negative behavioral aspects of the system. Using edge and edge-pair coverage as an acceptance criteria we identify and systematically derive abstract tests (high level user scenario) from models. Abstract tests are further refined and transformed into concrete tests with detailed steps and data. Although derivation of abstract tests and their transformation into concrete ones are manual, execution of concrete tests and generation of test report are automated. In total, we extracted 85 abstract test cases which resulted in 80 concrete test cases with over 550 iterations. Automated execution of all tests takes about 1 hour, while manual execution of one takes about 5 minutes (over 40 times speedup). MBT contributed to shift the focus of our intellectual work effort into model design rather than test case design, thus making derivation of test scenarios systematic and straight forward. In addition, applying MBT augmented and extended our traditional quality assurance techniques by facilitating better comprehension of new privacy and authorization concepts. Graphical models served as a useful aid in learning these concepts for newcomers.

We present a process driven test automation solution which enables delegating (part of) automation tasks from test automation engineer (expensive resource) to test analyst (non-developer, less expensive). In our approach, a test automation engineer implements test steps (or actions) which are executed automatically. Such automated test steps represent user actions in the system under test and specified by a natural language which is understandable by a non-technical person. Then, a test analyst with a domain knowledge organizes automated steps combined with test input to create an automated test case. It should be emphasized that the test analyst does not need to possess programming skills to create, modify or execute automated test cases. We refine benchmark test automation architecture to be better suitable for an effective separation and sharing of responsibilities between the test automation engineer (with coding skills) and test analyst (with a domain knowledge). In addition, we propose a metric to empirically estimate cooperation between test automation engineer and test analyst's works. The proposed automation solution has been defined based on our experience in the development and maintenance of Helsenorg, the national electronic health services in Norway which has had over one million of visits per month past year, and we still use it to automate the execution of regression tests.

A renewable fingerprint minutiae template generation scheme is proposed to utilize random projection for template diversification in a security enhanced way. The scheme first achieves absolute pre-alignment over local minutiae quadruplets in the original template and results in a fix-length feature vector; and then encrypts the feature vector by projecting it to multiple random matrices and quantizing the projected result; and finally post-process the resultant binary vector in a size and security tunable way to obtain the final protected minutia vicinity. Experiments on the fingerprint database FVC2002DB2_A demonstrate the desirable biometric performance of the proposed scheme.

Often in the development of a biometric product an evaluator of the system is the same entity who developed the algorithm. Moreover, usually the test data employed in such evaluation is also collected by the same developer/evaluator. In most cases such database will not be made public and consequently test results cannot be verified by independent institutions. This paper presents an independent report on fingerprint performance evaluation that has been conducted in the context of the TURBINE project. In this study the algorithm developer and system evaluator are represented by separate and independent entities. In addition, the algorithm developer does not have access to the primary test database. All these provide pre-conditions to unbiased and trustworthy performance reports. Furthermore, this paper introduces biometric performance testing on a level of biometric references, which is complementary to image- or minutiae-based references. Biometric references in the TURBINE project are pseudonymous identifiers that have been generated by the template protection algorithms. The results of the performance evaluation in this paper are generated by applying the algorithm developers (binary) algorithms at the minutiae (traditional) and pseudonymous identifier levels. The test data set consists of almost 72000 fingerprint images from 100 subjects acquired by several fingerprint scanners to which the algorithm developers did not have access.

In this paper, we provide some insights towards understanding the uniqueness of gait (ankle motion) by relating the discriminativeness of gait to the shoe type, direction of the motion and gait cycle. For analysis, we use gait samples of the people when all of them walk with the same specific types of footwear, thus eliminating the randomness (noise) introduced by the shoe variability. We collect gait using an accelerometer sensor, which is attached to the ankle of the person. The accelerometer records ankle motion in three directions: up-down, forward-backward and sideway. The verification method is based on detecting and averaging gait cycles in acceleration signal. Our gait data set consists of 480 samples from 30 persons. Each person walked with the 4 different types of footwear. Our analysis reveal that heavy footwear reduces the discrimination and the sideway motion of the foot has the most discriminating power compared to the up-down or forward-backward directions of the motion. Furthermore, various gait cycle parts contribute differently to the recognition performance. In addition, our analysis confirm that recognition performance can significantly decrease when the test and template samples are obtained using different shoe types. The recognition performance in terms of EER was in the range of 5%-18.3% mainly depending on the shoe type and the direction of motion.

Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system.

This paper presents an approach on recognising individuals based on 3D acceleration data from walking, which are collected using MEMS. Unlike most other gait recognition methods, which are based on video source, our approach uses walking acceleration in three directions: vertical, backward-forward and sideways. Using gait samples from 21 individuals and applying two methods, histogram similarity and cycle length, the equal error rates of 5% and 9% are achieved, respectively.