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Adaptive Authentication allows a system to dynamically select the best mechanism(s) for authenticating a user depending on contextual factors, such as location, proximity to devices, and other attributes. Though this technology has the potential to change the current password-dominated authentication landscape, research to date has not led to practical solutions that transcend to our daily lives. Motivated to find out how to improve adaptive authentication design, we provide a structured survey of the existing literature to date and analyze it to identify and discuss current research challenges and future directions.

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.

The cloud computing paradigm is set to become the next explosive revolution on the Internet, but its adoption is still hindered by security problems. One of the fundamental issues is the need for better access control and identity management systems. In this context, Federated Identity Management (FIM) is identified by researchers and experts as an important security enabler, since it will play a vital role in allowing the global scalability that is required for the successful implantation of cloud technologies. However, current FIM frameworks are limited by the complexity of the underlying trust models that need to be put in place before inter-domain cooperation. Thus, the establishment of dynamic federations between the different cloud actors is still a major research challenge that remains unsolved. Here we show that risk evaluation must be considered as a key enabler in evidencebased trust management to foster collaboration between cloud providers that belong to unknown administrative domains in a secure manner. In this paper, we analyze the Federated Identity Management process and propose a taxonomy that helps in the classification of the involved risks in order to mitigate vulnerabilities and threats when decisions about collaboration are made. Moreover, a set of new metrics is

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