Cloud computing allows accessing resources across Internet transparently: requiring no expertise in, or control over the underlying infrastructure. There is an increasing interest in sharing media files with family and friends. However, UPnP or DLNA were not designed for media distribution beyond the boundaries of a local network and manage media files through web applications can be tedious. To overcome this problem, we propose Media Cloud, a middleware for Set-top boxes for classifying, searching, and delivering media inside home network and across the cloud that interoperates with UPnP and DLNA.

Transport Layer Security is becoming the de facto standard to provide end-to-end security in the current Internet. IoT and M2M scenarios are not an exception since TLS is also being adopted there. The ability of TLS for negotiating any security parameter, its flexibility and extensibility are responsible for its wide adoption but also for several attacks. Moreover, as it relies on Public Key Infrastructure (PKI) for authentication, it is also affected by PKI problems. Considering the advent of IoT/M2M scenarios and their particularities, it is necessary to have a closer look at TLS history to evaluate the potential challenges of using TLS and PKI in these scenarios. According to this, the article provides a deep revision of several security aspects of TLS and PKI, with a particular focus on current Certificate Pinning solutions in order to illustrate the potential problems that should be addressed.

Pervasive devices interacting in open and dynamic spaces with each others require a mechanism that allows them acting autonomously in a secure way and protecting their resources. Trust is fundamental to establish communication with other users, because the identity is often uncertain and on one's own does not provide trust information, for instance, could an unknown user be trustworthy? Nowadays, these devices have a so limited security support. So, we propose a simple trust management model to enhance such support, allowing them interact in ad hoc networks and peer-to-peer applications in a secure way. In this paper, our main contribution is a mathematical and a probabilistic model, as well as demonstrating the model feasibility, since it has been assessed through the prototype implementation, which has been tested in a Pocket PC.

Consumer cloud computing paradigm has emerged as the natural evolution and integration of advances in several areas including distributed computing, service oriented architecture and consumer electronics. In this complex ecosystem, security and identity management challenges have cropped up, given their dynamism and heterogeneity. As a direct consequence, dynamic federated identity management with privacy improvements has arisen as an indispensable mechanism to enable the global scalability and usability that are required for the successful implantation of Cloud technologies. With these requirements in mind, we present an IdM architecture based on privacy and reputation extensions compliance with the SAMLv2/ID-FF standards¹.

The Internet of Things will bring a scenario in which interaction between humans and devices will be critical to allow people to use, monitor or configure Internet of Things devices. Interactions in such applications are based on traditional graphical interfaces. Devices that accept interaction based on Natural Language, e.g., through voice commands, can understand basic human orders or answering questions whenever user expressions fit into a known language pattern. Some devices can understand natural language voice commands but require sophisticated voice assistants located in the cloud, which raises significant privacy concerns. Others devices which handle voice-processing locally can perform a very limited local recognition system, requiring users to be familiar with words the system can process. The purpose of this work is to diminish the complexity of Natural Language processing in the context of IoT. The solution posited in this article allows Internet of Things devices to offload Natural Language processing to a system that improves the use of Natural Language and alleviates the need to learn or remember specific words or terms intended for triggering device actions. We have evaluated the feasibility of the design with a proof-of-concept implemented in a home environment and it was tested by real users.

Collaborative healthcare environments offer potential benefits, including enhancing the healthcare quality delivered to patients and reducing costs. As a direct consequence, sharing of electronic health records (EHRs) among healthcare providers has experienced a noteworthy growth in the last years, since it enables physicians to remotely monitor patients' health and enables individuals to manage their own health data more easily. However, these scenarios face significant challenges regarding security and privacy of the extremely sensitive information contained in EHRs. Thus, a flexible, efficient, and standards-based solution is indispensable to guarantee selective identity information disclosure and preserve patient's privacy. We propose a privacy-aware profile management approach that empowers the patient role, enabling him to bring together various healthcare providers as well as user-generated claims into an unique credential. User profiles are represented through an adaptive Merkle Tree, for which we formalize the underlying mathematical model. Furthermore, performance of the proposed solution is empirically validated through simulation experiments.