Dangers of ‘Facebook Login’ for Mobile Apps: Is There a Price Tag for Social Information?

Social networks offer horizontal integration for any mobile platform providing app users with a convenient single sign-on point. Nonetheless, there are growing privacy concerns regarding its use. These vulnerabilities trigger alarm among app developers who fight for their user base: While they are happy to act on users’ information collected via social networks, they are not always willing to sacrifice their adoption rate for this goal. So far, understanding of this trade-off has remained ambiguous. To fill this gap, we employ a discrete choice experiment to explore the role of Facebook Login and investigate the impact of accompanying requests for different information items / actions in the mobile app adoption process. We quantify users’ concerns regarding these items in monetary terms. Beyond hands-on insights for providers, our study contributes to the theoretical discourse on the value of privacy in the growing world of Social Media and mobile web.

Privacy Panel: Usable and Quantifiable Mobile Privacy

The ever increasing popularity of apps stems from their ability to provide highly customized services to the user. The flip side is that in order to provide such services, apps need access to very sensitive private information about the user. This leads to malicious apps that collect personal user information in the background and exploit it in various ways. Studies have shown that current app vetting processes which are mainly restricted to install time verification mechanisms are incapable of detecting and preventing such attacks. We argue that the missing fundamental aspect here is a comprehensive and usable mobile privacy solution, one that not only protects the user's location information, but also other equally sensitive user data such as the user's contacts and documents. A solution that is usable by the average user who does not understand or care about the low level technical details. To bridge this gap, we propose privacy metrics that quantify low-level app accesses in terms of privacy impact and transforms them to high-level user understandable ratings. We also provide the design and architecture of our Privacy Panel app that represents the computed ratings in a graphical user-friendly format and allows the user to define policies based on them. Finally, experimental results are given to validate the scalability of the proposed solution.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

Source of funding and results of studies of health effects of mobile phone use: systematic review of experimental studies

OBJECTIVES: There is concern regarding the possible health effects of cellular telephone use. We examined whether the source of funding of studies of the effects of low-level radiofrequency radiation is associated with the results of studies. We conducted a systematic review of studies of controlled exposure to radiofrequency radiation with health-related outcomes (electroencephalogram, cognitive or cardiovascular function, hormone levels, symptoms, and subjective well-being). DATA SOURCES: We searched EMBASE, Medline, and a specialist database in February 2005 and scrutinized reference lists from relevant publications. DATA EXTRACTION: Data on the source of funding, study design, methodologic quality, and other study characteristics were extracted. The primary outcome was the reporting of at least one statistically significant association between the exposure and a health-related outcome. Data were analyzed using logistic regression models. DATA SYNTHESIS: Of 59 studies, 12 (20%) were funded exclusively by the telecommunications industry, 11 (19%) were funded by public agencies or charities, 14 (24%) had mixed funding (including industry), and in 22 (37%) the source of funding was not reported. Studies funded exclusively by industry reported the largest number of outcomes, but were least likely to report a statistically significant result: The odds ratio was 0.11 (95% confidence interval, 0.02-0.78), compared with studies funded by public agencies or charities. This finding was not materially altered in analyses adjusted for the number of outcomes reported, study quality, and other factors. CONCLUSIONS: The interpretation of results from studies of health effects of radiofrequency radiation should take sponsorship into account.

Cervical soft tissue imaging using a mobile CBCT scanner with a flat panel detector in comparison with corresponding CT and MRI data sets

OBJECTIVE: The aim of this study was to evaluate soft tissue image quality of a mobile cone-beam computed tomography (CBCT) scanner with an integrated flat-panel detector. STUDY DESIGN: Eight fresh human cadavers were used in this study. For evaluation of soft tissue visualization, CBCT data sets and corresponding computed tomography (CT) and magnetic resonance imaging (MRI) data sets were acquired. Evaluation was performed with the help of 10 defined cervical anatomical structures. RESULTS: The statistical analysis of the scoring results of 3 examiners revealed the CBCT images to be of inferior quality regarding the visualization of most of the predefined structures. Visualization without a significant difference was found regarding the demarcation of the vertebral bodies and the pyramidal cartilages, the arteriosclerosis of the carotids (compared with CT), and the laryngeal skeleton (compared with MRI). Regarding arteriosclerosis of the carotids compared with MRI, CBCT proved to be superior. CONCLUSIONS: The integration of a flat-panel detector improves soft tissue visualization using a mobile CBCT scanner.

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

A Case for CDN-as-a-Service in the Cloud: A Mobile Cloud Networking Argument

Content Distribution Networks are mandatory components of modern web architectures, with plenty of vendors offering their services. Despite its maturity, new paradigms and architecture models are still being developed in this area. Cloud Computing, on the other hand, is a more recent concept which has expanded extremely quickly, with new services being regularly added to cloud management software suites such as OpenStack. The main contribution of this paper is the architecture and the development of an open source CDN that can be provisioned in an on-demand, pay-as-you-go model thereby enabling the CDN as a Service paradigm. We describe our experience with integration of CDNaaS framework in a cloud environment, as a service for enterprise users. We emphasize the flexibility and elasticity of such a model, with each CDN instance being delivered on-demand and associated to personalized caching policies as well as an optimized choice of Points of Presence based on exact requirements of an enterprise customer. Our development is based on the framework developed in the Mobile Cloud Networking EU FP7 project, which offers its enterprise users a common framework to instantiate and control services. CDNaaS is one of the core support components in this project as is tasked to deliver different type of multimedia content to several thousands of users geographically distributed. It integrates seamlessly in the MCN service life-cycle and as such enjoys all benefits of a common design environment, allowing for an improved interoperability with the rest of the services within the MCN ecosystem.

GoCARB: Smartphone-App zur Kohlenhydratberechnung bei Typ-1-Diabetes

Smartphone-App zur Kohlenhydratberechnung Neue Technologien wie Blutzuckersensoren und moderne Insulinpumpen prägten die Therapie des Typ-1-Diabetes (T1D) in den letzten Jahren in wesentlichem Ausmaß. Smartphones sind aufgrund ihrer rasanten technischen Entwicklung eine weitere Plattform für Applikationen zur Therapieunterstützung bei T1D. GoCARB Hierbei handelt es sich um ein zur Kohlenhydratberechnung entwickeltes System für Personen mit T1D. Die Basis für Endanwender stellt ein Smartphone mit Kamera dar. Zur Berechnung werden 2 mit dem Smartphone aus verschiedenen Winkeln aufgenommene Fotografien einer auf einem Teller angerichteten Mahlzeit benötigt. Zusätzlich ist eine neben dem Teller platzierte Referenzkarte erforderlich. Die Grundlage für die Kohlenhydratberechnung ist ein Computer-Vision-gestütztes Programm, das die Mahlzeiten aufgrund ihrer Farbe und Textur erkennt. Das Volumen der Mahlzeit wird mit Hilfe eines dreidimensional errechneten Modells bestimmt. Durch das Erkennen der Art der Mahlzeiten sowie deren Volumen kann GoCARB den Kohlenhydratanteil unter Einbeziehung von Nährwerttabellen berechnen. Für die Entwicklung des Systems wurde eine Bilddatenbank von mehr als 5000 Mahlzeiten erstellt und genutzt. Resümee Das GoCARB-System befindet sich aktuell in klinischer Evaluierung und ist noch nicht für Patienten verfügbar.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.