Arnaud Oglaza

A survey on addressing privacy together with quality of context for context management in the Internet of Things

Making the Internet of Things (IoT) a reality will contribute to extend the context-aware ability of numerous sensitive applications. We can foresee that the context of users will include not only their own spatio-temporal conditions but also those of the things situated in their ambient environment and at the same time, thanks to the IoT, those that are located in other remote spaces. Consequently, next-generation context managers have to interact with the IoT underlying technologies and must, even more than before, address both privacy and quality of context (QoC) requirements. In this article, we show that the notions of privacy and QoC are intimately related and sometimes contradictory and survey the recent works addressing them. Current solutions usually consider only one notion, and very few of them started to bridge privacy and QoC. We identify some of the remaining challenges that next-generation context managers have to deal with to favour users’ acceptability by providing both the optimal QoC level and the appropriate privacy protection.

Difficulties to enforce your privacy preferences on Android? Kapuer will help you

Smartphones and mobile computing have changed our world and we are now over connected. Millions of applications are available to help us in every way possible. However applications can collect data from users for different purposes. Many private data are used to profile users. How to control privacy in this environment? We propose a system called Kapuer that improves the management of applications permissions on Android by combining access control and decision support. We present in this article the Android implementation of Kapuer.

Kapuer: A Decision Support System for Protecting Privacy

Pervasive computing allows a world full of electronic devices connected to each other, autonomous, context aware and with a certain level of intelligence. They are deployed in our environment to ease our life. However today users don’t control the traffic around their data. The use of mobile devices might increase this problem because the system is more complex and requests of personal data is transparent to users in order to reduce their cognitive load. The goal of Kapuer is to inform the user on requests about his privacy and help him protect his privacy by assisting him in the writing of authorization policies. But a risk remains, informing too much the user can drown him into a huge amount of information and could quit using the system. The idea around Kapuer is to make the user conscious of the situation without bothering him too much.

KAPUER : un assistant à l'écriture de politiques d'autorisation pour la protection de la vie privée

Nous utilisons de plus en plus d’equipements informatique connectes a Internet. Nos telephones, nos tablettes, et maintenant les equipements de notre quotidien peuvent desormais partager des informations pour faciliter notre vie. Partager ces donnees peut porter prejudice a notre vie privee et il est necessaire de les controler. Cependant, cette tâche est complexe surtout pour des utilisateurs novices. Pour les y aider, nous presentons un systeme d’aide a la decision appele KAPUER dont l’objectif est d’apprendre les preferences en termes de protection de la vie privee et de proposer des regles adaptees pour le controle de l’acces aux donnees. Ce systeme est integre dans l’architecture de gestion d’autorisation XACML et trois algorithmes d’apprentissages sont evalues. We are using more and more devices connected to the Internet. Our smartphones, tablets and now everyday items can share data to make our life easier. Sharing data may harm our privacy and there is a need to control them. However, this task is complex especially for non technical users. To facilitate this task, we present a decision support system, named KAPUER, that proposes high level authorization policies by learning users’ privacy preferences. KAPUER has been integrated into XACML and three learning algorithms have been evaluated.