Cryptographic keys based on biometrics, besides offering information security and privacy, have the advantage of being strongly linked to the user. Three main techniques use biometric data to obtain cryptographic keys: cryptographic key release, cryptographic key generation and cryptographic key regeneration. The main objectives of these schemes are to ensure revocability and produce keys with high entropy. In this work, we propose a new technique for secret key agreement based on biometrics. Three security factors are used, represented by an iris code, an RFID tag and a password. The use of the protocol for reconciliation between the RFID reader and tag enables the agreement of a symmetric key with high entropy by discarding all the mismatching bits present in genuine samples, while it fails to do so for impostors that are therefore rejected. A new secret key is agreed after every positive authentication, increasing the security of the system. The system was evaluated on the public database ICE2005 and obtained a 270 binary digit cryptographic key with estimated entropy of about 156 bits at 0% False Acceptance Rate (FAR) and 3.68% False Rejection Rate (FRR).

Energy availability is the main delimiter for usage time of mobile devices. As wireless network interfaces are usually the most power-demanding subsystem in a mobile device, mechanisms for their power consumption optimization are extensively researched. However, determining the power saving gains from using such optimizations is not an easy task, specially for simulation-based analysis. This work presents an extended power consumption model for the WiMAX interface in Mobile Nodes (MN), capable of capturing minimal contributions such as signalling overhead or burst allocation geometry. Simulation results show an effectiveness comparison of discussed power consumption models.

In Daugman's iris recognition method, the application points determine which pixels of the normalized iris images will be used in the matching stage of the algorithm. In his work, those points are chosen in an equidistant form, referenced here as homogeneous distribution. The homogeneous distribution of these points, often selects pixels that represent eyelids, eyelashes and specular reflections, occlusions that should be extracted from the matching step. A binary mask (occlusion mask), in the matching step, enables disregarding the computation of these bits. However, some template protection schemes have restrictions on the use of such masks, either because of memory/computational cost limitations or because of limitations of the algorithm itself. In this paper, we propose a method that optimizes the distribution of the application points avoiding regions with high rate of occlusions, reducing the impact of not using the occlusion mask in the matching step. The method is based on statistical analysis. The new application points distribution is called optimal distribution. The recognition performance obtained with the optimal distribution of the application points was EER = 3.1% and F RR = 6.3% (for F AR = 0.1%) while for the homogeneous distribution without the usage of masks EER = 4.8% and F RR = 12.7% (for F AR = 0.1%).