Model checking probabilistic and stochastic extensions of the pi-calculus

We present an implementation of model checking for probabilistic and stochastic extensions of the pi-calculus, a process algebra which supports modelling of concurrency and mobility. Formal verification techniques for such extensions have clear applications in several domains, including mobile ad-hoc network protocols, probabilistic security protocols and biological pathways. Despite this, no implementation of automated verification exists. Building upon the pi-calculus model checker MMC, we first show an automated procedure for constructing the underlying semantic model of a probabilistic or stochastic pi-calculus process. This can then be verified using existing probabilistic model checkers such as PRISM. Secondly, we demonstrate how for processes of a specific structure a more efficient, compositional approach is applicable, which uses our extension of MMC on each parallel component of the system and then translates the results into a high-level modular description for the PRISM tool. The feasibility of our techniques is demonstrated through a number of case studies from the pi-calculus literature

On the asynchronous nature of communication in concurrent logic languages : a fully abstract model based on sequences

On the asynchronous nature of communication model based on sequences b

Online Sensitivity Optimization in Differentially Private Learning

Training differentially private machine learning models requires constraining an individual’s contribution to the optimization process. This is achieved by clipping the 2-norm of their gradient at a predetermined threshold prior to averaging and batch sanitization. This selection adversely influences optimization in two opposing ways: it either exacerbates the bias due to excessive clipping at lower values, or augments sanitization noise at higher values. The choice significantly hinges on factors such as the dataset, model architecture, and even varies within the same optimization, demanding meticulous tuning usually accomplished through a grid search. In order to circumvent the privacy expenses incurred in hyperparameter tuning, we present a novel approach to dynamically optimize the clipping threshold. We treat this threshold as an additional learnable parameter, establishing a clean relationship between the threshold and the cost function. This allows us to optimize the former with gradient descent, with minimal repercussions on the overall privacy analysis. Our method is thoroughly assessed against alternative fixed and adaptive strategies across diverse datasets, tasks, model dimensions, and privacy levels. Our results indicate that it performs comparably or better in the evaluated scenarios, given the same privacy requirements

Structural operational semantics for Kernel Andorra Prolog

Kernel Andorra Prolog is a framework for nondeterministic concurrent constraint logic programming languages. Many languages, such as Prolog, GHC, Parlog, and Atomic Herbrand, can be seen as instances of this framework, by adding specific constraint systems and constraint operations, and optionally by imposing further restrictions on the language and the control of the computation model. We systematically revisit the description in Haridi and Jarison [HJ90], adding the formal machinery which is necessary in order to completely formalize the control of the computation model. To this we add a formal description of the transformational semantics of Kernel Andorra Prolog. The semantics of Kernel Andorra Prolog is a set of or-trees which also captures infinite computations

Semantic models for concurrent logic languages

AbstractIn this paper we develop semantic models for a class of concurrent logic languages. We give two operational semantics based on a transition system, a declarative semantics and a denotational semantics. One operational and the declarative semantics model the success set, that is, the set of computed answer substitutions corresponding to all successfully terminating computations. The other operational and the denotational semantics also model deadlock and infinite computations. For the declarative and the denotational semantics we extend standard notions such as unification in order to cope with the synchronization mechanism of the class of languages we study. The basic mathematical structure for the declarative semantics is the complete lattice of sets of finite streams of substitutions. In the denotational semantics, we use a complete metric space of tree-like structures that are labelled with functions that represent the basic unification step. We look at the relations between the different models. We relate first the two operational semantics and next the declarative and denotational semantics with their respective operational counterparts