Improving Scalability And Reusability Of Differential Cryptanalysis Models Using Constraint Programming
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Improving Scalability and Reusability of Differential Cryptanalysis Models Using Constraint Programming
In this thesis, we are interested in the use of constraint programming (CP) for solving differential cryptanalysis problems. In particular, we are interested in differential (related or single key) characteristic search problems for the symmetric encryption algorithms Rijndael, AES and Midori. We have also modelled boomerang attacks for Rijndael and generalized this method to Feistel schemes. This new modelling has been tested on WARP, Twine and LBlock-s encryption. To solve these different problems, we have proposed new techniques combining SAT and CP solvers. We have also introduced a new global constraint to more efficiently propagate a set of XOR constraints when searching for truncated differential characteristics. These new models have allowed us to improve the performance of existing solutions and to discover new distinguishers for WARP (23 rounds), Twine (15 and 16 rounds) and LBlock-s (16 rounds). We also found new attacks on Rijndael (9 rounds with the 128-160 version, 12 rounds with the 128-224 and 160-256 versions) and on WARP (26 rounds).
Ant Colony Optimization and Constraint Programming
Author: Christine Solnon
language: en
Publisher: John Wiley & Sons
Release Date: 2013-03-04
Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. The second part describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part shows how the ant colony may be integrated within a constraint programming language, thus combining the expressive power of constraint programming languages, to describe problems in a declarative way, and the solving power of ant colony optimization to efficiently solve these problems.
Constraint-Based Scheduling
Author: Philippe Baptiste
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-07-31
Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsible for the "hardness" of the scheduling problem. Chapters 6, 7, and 8 are dedicated to the resolution of several scheduling problems. These examples illustrate the use and the practical efficiency of the constraint propagation methods of the previous chapters. They also show that besides constraint propagation, the exploration of the search space must be carefully designed, taking into account specific properties of the considered problem (e.g., dominance relations, symmetries, possible use of decomposition rules). Chapter 9 mentions various extensions of the model and presents promising research directions.