Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

Soft biometrics through hand gestures driven by visual stimuli

We present a novel biometric solution which exploits hand gestures, tracked by the Microsoft Kinect sensor, performed in response to a circle randomly appearing in five predefined screen positions. Features of both hand and screen pointer are used for classification purposes, considering both the whole 20-path trajectory and shorter routes. In particular, we search for the "optimal" trajectory length which assures a good trade-off between precision and user effort. For identification, the approach achieves classification accuracies ranging from 0.748 to 0.942. For verification, accuracy is still satisfactory (always higher than 0.962), despite moderate specificity values. Keywords: Soft biometrics, Gestures, Visual stimul

A new dataset for smartphone gesture-based authentication

In this paper, we consider the problem of authentication on a smartphone, based on gestures. Specifically, the gestures consist of users holding a smartphone while writing their initials in the air. Accelerometer data from 80 subjects was collected and we provide a preliminary analysis of this data using machine learning techniques. The machine learning techniques considered include principal component analysis (PCA) and support vector machines (SVM). The results presented here are intended to provide a baseline for additional research based on our dataset

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals

Security Issues in mGovernment

E-government is one of the most rapidly evolving service domains in the contemporary information society. Many governments have already developed and provided e-government services to businesses and citizens. Nowadays actors in the government domain attempt to take the next step and exploit the latest wireless technologies in order to provide ubiquitous services for mobile users. However, this approach involves some hidden risks mainly due to the inherent insecurity of the air medium and the vulnerabilities of the wireless systems. Thus, in this paper we investigate the security gaps and considerations which should be taken into account for an m-government system. Finally, we provide a list of security guidelines and policies, which the users of the system should be aware of and follow in order to avoid security attacks