Hierarchical Combination of Intruder Theories

Abstract. Recently automated deduction tools have proved to be very effective for detecting attacks on cryptographic protocols. These analysis can be improved, for finding more subtle weaknesses, by a more accurate modelling of operators employed by protocols. Several works have shown how to handle a single algebraic operator (associated with a fixed intruder theory) or how to combine several operators satisfying disjoint theories. However several interesting equational theories, such as exponentiation with an abelian group law for exponents remain out of the scope of these techniques. This has motivated us to introduce a new notion of hierarchical combination for intruder theories and to show decidability results for the deduction problem in these theories. Under a simple hypothesis, we were able to simplify this deduction problem. This simplification is then applied to prove the decidability of constraint systems w.r.t. an intruder relying on exponentiation theory.

The Correspondence between the Logical Algorithms Language and CHR

This paper investigates the relationship between the Logical Algorithms formalism (LA) of Ganzinger and McAllester and Constraint Handling Rules (CHR). We present a translation scheme from LA to CHR rp: CHR with rule priorities and show that the metacomplexity theorem for LA can be applied to a subset of CHR rp via inverse translation. This result is compared with previous work. Inspired by the high-level implementation proposal of Ganzinger and McAllester, we demonstrate how LA programs can be compiled into CHR rules that interact with a scheduler written in CHR. This forms the first actual implementation of LA. Our implementation achieves the required complexity for the meta-complexity theorem to hold and can execute a subset of CHR rp with strong complexity bounds

Boosting with diverse base classifiers

Abstract. We establish a new bound on the generalization error rate of the Boost-by-Majority algorithm. The bound holds when the algorithm is applied to a collection of base classifiers that contains a "diverse " subset of "good " classifiers, in a precisely defined sense. We describe cross-validation experiments that suggest that Boost-by-Majority can be the basis of a practically useful learning method, often improving on the generalization of AdaBoost on large datasets

Normalizable Horn Clauses, Strongly Recognizable Relations, and Spi

Abstract. We exhibit a rich class of Horn clauses, which we call H1, whose least models, though possibly infinite, can be computed effectively. We show that the least model of an H1 clause consists of so-called strongly recognizable relations and present an exponential normalization procedure to compute it. In order to obtain a practical tool for program analysis, we identify a restriction of H1 clauses, which we call H2, where the least models can be computed in polynomial time. This fragment still allows to express, e.g., Cartesian product and transitive closure of relations. Inside H2, we exhibit a fragment H3 where normalization is even cubic. We demonstrate the usefulness of our approach by deriving a cubic control-flow analysis for the Spi calculus [1] as presented in [14]