The SDN paradigm allows networks to be dynamically reconfigurable by network applications. SDN is also of particular interest for NFV which deals with the virtualization of network functions. The network programmability offered by SDN presents then various advantages but it also induces various threats regarding potential attacks on the network. For instance, there is a critical risk that a hacker takes over the network control by exploiting this SDN network programmability (e.g., using the SDN API or tampering a network application running on the SDN controller). This paper proposes then an approach to deal with this possible lack of trust in the SDN controller or in their applications. This approach consists in not relying on a single controller but on several ‘redundant’ controllers that may also run in different execution environments. The network configuration requests coming from these controllers are then compared and, if deemed sufficiently consistent and then trustable, they are actually sent to the network. This approach has been implemented in an intermediary layer (based on a network hypervisor) inserted between the network equipments and the controllers. Experimentations have been performed showing the feasibility of the approach and providing some first evaluations of its impact on the network and the services.

Cloud Computing offers great benefits such as reduced IT costs and an improved business agility. Nevertheless, enterprises are still hesitant to put their sensitive data in the cloud as they notably fear privacy issues (e.g., violation of country-based regulations regarding the storage location of a sensitive data). In this context, this paper presents the demonstration of a privacy control technology that allows to protect sensitive files stored, processed, and moved in an IaaS cloud. In our approach, the privacy control is performed within the file system of the Virtual Machines (VM) and allows to control the access done by any application to each sensitive file. It notably covers business applications (e.g., provided by the cloud user) and system applications such as FTP (e.g., to prevent the transfer of a sensitive file in a not authorized country). Moreover, our technology allows to generate tamper-proof traces for any action performed on a sensitive file. In the demonstration, we then also show how the cloud user has a full view of the usage of his sensitive files (e.g., number of copies, storage locations, performed actions). Finally, the demonstration shows these different capabilities through a scenario of file access and cross-country transfer in a multi-platform cloud environment.

In this paper, we present a privacy control mechanism called PDE (Privacy Data Envelope) allowing users to protect their privacy sensitive content travelling over social and communication networks. Our solution is based on privacy policies expressed by the user and associated with his content. This approach makes use of a decentralized architecture carried out through a PDE feature that has to be added to the existing application access tools like email clients and web browsers. A prototype has been developed to embody the PDE paradigm and to illustrate a scenario where such envelopes cross the boundaries of enterprise social networks and other communications tools. Preliminary performance evaluations were done helping the understanding of the PDE plug-in behaviors and computation overhead.

Access to relevant information, adapted to user's needs, preferences and environment, is a challenge in many applications running in content delivery platforms, like IPTV, VoD and mobile Video. In order to provide users with personalized content, applications use various techniques such as content recommendation, content filtering, preference-driven queries, etc. These techniques exploit different knowledge organized into profiles and contexts. However, there is not a common understanding of these concepts and there is no clear foundation of what a personalized access model should be. This paper contributes to this concern by providing, through a meta model, a clear distinction between profile and context, and by providing a set of services which constitutes a basement to the definition of a personalized access model (PAM). Our PAM definition allows applications to interoperate in multiple personalization scenarios, including, preference-based recommendation, context-aware content delivery, personalized access to multiple contents, etc. Concepts and services proposed are tightly defined with respect to real applications requirements provided by Alcatel-Lucent.

This paper proposes enhancements for information modeling to support service and network management integration. Despite the existence of management standard models (TMN, TMF, DMTF), it seems that operators are facing difficulties choosing the appropriate model, defining its instances and achieving its implementations. The analysis of this situation stresses the need for an information model able to represent the real world objects independently of their management. To respond to this key requirement, we propose solutions, based on a metamodel, that guarantee information coherence, self-management capability and management integration. These propositions allow the operators to support new workflows and to handle new services efficiently. Furthermore, the M3100 model, the MTNM model and the ENST model are applied to model an ADSL network example.

Building on previous work on incremental natural scene modelling for mobile robot navigation, we focus in this paper on the problem of representing and managing uncertainties. The environment is composed of ground regions and objects. Objects (e.g., rocks) are represented by an uncertain state vector (location) and a variance-covariance matrix. Their shapes are approximated by ellipsoids. Landmarks are defined as objects with specific properties (discrimination, accuracy) that permit to use them for robot localization and for anchoring the environment model. Model updating is based on an extended Kalman filter. Experimental results are given that show the construction of a consistent model over tens of meters.

A mobile robot navigates in an environment and incrementally builds a map while locating itself. The quality of the map depends on the sensor models. Due to the difficulty of defining a stochastic odometric model, the classical approach based on Kalman filtering is revisited in order to decouple the information provided by the odometric sensor and exteroceptive sensors. The proposed approach is based on the observation and fusion of geometric relationships between objects. These relationships are represented in local maps and related in a global map where odometric information is exploited. The resulting local maps are invariant with respect to biases introduced in the robot location (position and orientation) due to sliding for instance. Experimental results on real data compare both the classical and the proposed approach.

Mobile robot navigation in natural environments requires incremental scene modelling and robot self-localization. These two capacities are discussed in this paper. A scene model is built with heterogeneous representations to match the diversity of the environment. We focus on object-based representations, using coarse models (ellipsoids) or volumic primitives (superquadrics), and their topological relations. The method for extracting the models from a range image to provide geometrical and topological representations, and the matching of successive views during the robot's movement are presented. For robot localization, two procedures are proposed based on the coarse or fine models. Experimental results are given.

In this paper, we focus on some perceptual functions required by a generic task "GOTO" in natural environments; in previous works, only geometrical modeling has been used to deal with two fundamental tasks: landmark extraction and recognition for sensor-based motion control or robot localization, and terrain modeling for motion planning. Geometrical representations alone lead to a bulky model, and, after some iterations, to a combinatorial explosion. We present here, higher level representations; from a range image, given some assumptions on the perceived scene (even ground with few objects), we propose a segmentation algorithm to extract simple semantical representations for the ground and the objects; then, we can analyze the relative positions of the objects to build a topological scene description. Both models constitute the scene model, needed for further incremental environment modeling.>