Foundations of Security: Cryptography, Protocols, Trust

Joshua Guttman
Worcester Polytechnic University (USA)

Course site: http://web.cs.wpi.edu/~guttman/biss/

Abstract: Distributed or web-based systems raise challenging security problems, because different participants may use them for conflicting goals. A security infrastructure for a distributed system must provide mechanisms for mutual authentication between participants; for authorizing their actions; for delegating the right to act on behalf of a principal; and for ensuring confidentiality. This course will contain three main components. First, we will study the foundations of cryptography. This consists of a collection of definitions---based on games and on complexity for probabilistic computations---and constructions to achieve them. Second, security protocols, which use these cryptographic methods to achieve authentication, confidentiality, and related properties, raise challenges for analysis and design. Finally, we will consider access control, especially as represented in logical trust management theories.

Stochastic Process Algebras for Quantitative Analysis

Jane Hillston
University of Edinburgh (UK)

Course site: http://homepages.inf.ed.ac.uk/jeh/biss2013/

Abstract: Process algebras were originally conceived to provide models of the functional behaviour of concurrent systems, allowing the modeller to study the correctness properties of such systems. However, simple additions to a process algebra can make it suitable also to support the analysis of non-functional properties such as reliability, availability and performance. In this course we will consider stochastic process algebras in which the algebra is enriched with activities which have an associated stochastic duration. We will demonstrate how such a language can be used for performance modelling of computer systems and more general forms of analysis of dynamic behaviour. Moreover, we will show how the compositional structure of the process algebra can be used to help in the dynamic analysis. Being discrete state models, stochastic process algebra models are prone to the problem of state space explosion but recent results on fluid semantics allow automated approximation of extremely large systems using continuous state representations. This approach will also be explained and demonstrated. Throughout the course the concepts will be illustrated by examples.

Shape and Visual Apperance Acquisition for Photo-realistic Visualization

Fabio Ganovelli & Massimiliano Corsini
CNR (Italy)

Course site: http://vcg.isti.cnr.it/~ganovell/bertinoro

Program:
Prerequisites: Algebra, Advanced Calculus, Data Structure.
1. Algorithms and Data Structure for the Geometry Acquisition
   Introduction. Range maps and Polygonal Meshes. Notes on cellular complexes: geometry and topology.
1.1 Geometry measurement
   Triangulation. Time-of-flight. Image-based techniques.
1.2 Range maps alignment algorithms
   Global alignment: Feature-based techniques. 4PCS algorithm. Optimization-on-a-manifold (OOM). Refinement: Iterative Closest Point (ICP).
1.3 Mesh reconstruction algorithms
   Combinatorial techniques: zippering, ball pivoting, front advancing.
   Volumetric techniques: Marching Cubes (MC), Poisson reconstruction, Moving Least Square (MLS).
1.4 Mesh complexity and simplification algorithms
   Finding approximate representation that preserve geometry, topology and quality.
   Vertex clusterization. Quadric-based mesh simplification.

2. Visual Appearance Acquisition
2.1 Introduction
   Light-matter interaction. Radiometry in a nutshell. Bidirectional Reflectance Distribution Function (BRDF) and Bidirectional Surface Scattering Reflectance Distribution function (BSSRDF).
2.2 BRDF measurement
   Gonioreflectometer. Image-based estimation. BRDF factorization through spherical harmonics. BRDF models. Fitting.
2.3 Reflectance as N-dimensional function estimation
   Taxonomy. Reflectance Field. Bidirectional Texture Function (BTF). Surface Light Field. Reflection Transformation Imaging: polynomial (PTM) and hemispherical harmonics approximation (HSH).
2.5 Texture registration
   Multi-modal matching through feature-based (keypoints, lines) and statistical methods (mutual information). Shading problems, intrinsic images and blending (in brief).