Abstract
We introduce the concept of identity based key encapsulation to multiple parties (mID-KEM), and define a security model for it. This concept is the identity based analogue of public key KEM to multiple parties. We also analyse possible mID-KEM constructions, and propose an efficient scheme based on bilinear pairings. We prove our scheme secure in the random oracle model under the Gap Bilinear Diffie-Hellman assumption.

Abstract
An increasingly important design constraint for software running on ubiquitous computing devices is security, particularly against physical methods such as side-channel attack. One well studied methodology for defending against such attacks is the concept of indistinguishable functions which leak no information about program control flow since all execution paths are computationally identical. However, constructing such functions by hand becomes laborious and error prone as their complexity increases. We investigate techniques for automating this process and find that effective solutions can be constructed with only minor amounts of computational effort.

Abstract
One way of exploring protein unfolding events associated with the development of Amyloid diseases is through the use of multiple Molecular Dynamics Protein Unfolding Simulations. The analysis of the huge amount of data generated in these simulations is not a trivial task. In the present report, we demonstrate the use of Association Rules applied to the analysis of the variation profiles of the Solvent Accessible Surface Area of the 127 amino-acid residues of the protein Transthyretin, along multiple simulations. This allowed us to identify a set of 28 hydrophobic residues forming a hydrophobic cluster that might be essential in the unfolding and folding processes of Transthyretin.

Abstract
We tackle the problem of sequence classification using relevant subsequences found in a dataset of protein labelled sequences. A subsequence is relevant if it is frequent and has a minimal length. For each query sequence a vector of features is obtained. The features consist in the number and average length of the relevant subsequences shared with each of the protein families. Classification is performed by combining these features in a Bayes Classifier. The combination of these characteristics results in a multi-class and multi-domain method that is exempt of data transformation and background knowledge. We illustrate the performance of our method using three collections of protein datasets. The performed tests showed that the method has an equivalent performance to state of the art methods in protein classification.

Abstract
Considering the characteristics of biological sequence databases, which typically have a small alphabet, a very long length and a relative small size (several hundreds of sequences), we propose a new sequence mining algorithm (gIL). gIL was developed for linear sequence pattern mining and results from the combination of some of the most efficient techniques used in sequence and itemset mining. The algorithm exhibits a high adaptability, yielding a smooth and direct introduction of various types of features into the mining process, namely the extraction of rigid and arbitrary gap patterns. Both breadth or a depth first traversal are possible. The experimental evaluation, in synthetic and real life protein databases, has shown that our algorithm has superior performance to state-of-the art algorithms. The use of constraints has also proved to be a very useful tool to specify user interesting patterns.

Abstract