Probabilistic data flow analysis: a linear equational approach

Speculative optimisation relies on the estimation of the probabilities that certain properties of the control flow are fulfilled. Concrete or estimated branch probabilities can be used for searching and constructing advantageous speculative and bookkeeping transformations. We present a probabilistic extension of the classical equational approach to data-flow analysis that can be used to this purpose. More precisely, we show how the probabilistic information introduced in a control flow graph by branch prediction can be used to extract a system of linear equations from a program and present a method for calculating correct (numerical) solutions.Comment: In Proceedings GandALF 2013, arXiv:1307.416

Estimating the Maximum Information Leakage

none2noopenAldini, Alessandro; DI PIERRO, A.Aldini, Alessandro; DI PIERRO, A

A Quantitative Approach to Noninterference for Probabilistic Systems

n/

A new tool for the performance analysis of massively parallel computer systems

We present a new tool, GPA, that can generate key performance measures for very large systems. Based on solving systems of ordinary differential equations (ODEs), this method of performance analysis is far more scalable than stochastic simulation. The GPA tool is the first to produce higher moment analysis from differential equation approximation, which is essential, in many cases, to obtain an accurate performance prediction. We identify so-called switch points as the source of error in the ODE approximation. We investigate the switch point behaviour in several large models and observe that as the scale of the model is increased, in general the ODE performance prediction improves in accuracy. In the case of the variance measure, we are able to justify theoretically that in the limit of model scale, the ODE approximation can be expected to tend to the actual variance of the model

Measuring the confinement of probabilistic systems

AbstractIn this paper we lay the semantic basis for a quantitative security analysis of probabilistic systems by introducing notions of approximate confinement based on various process equivalences. We re-cast the operational semantics classically expressed via probabilistic transition systems (PTS) in terms of linear operators and we present a technique for defining approximate semantics as probabilistic abstract interpretations of the PTS semantics. An operator norm is then used to quantify this approximation. This provides a quantitative measure ɛ of the indistinguishability of two processes and therefore of their confinement. In this security setting a statistical interpretation is then given of the quantity ɛ which relates it to the number of tests needed to breach the security of the system

Probabilistic Constraint Handling Rules

Abstract Classical Constraint Handling Rules (CHR) provide a powerful tool for specifying and implementing constraint solvers and programs. The rules of CHR rewrite constraints (non-deterministically) into simpler ones until they are solved. In this paper we introduce an extension of Constraint Handling Rules (CHR), namely Probabilistic CHRs (PCHR). These allow the probabilistic "weighting" of rules, specifying the probability of their application. In this way we are able to formalise various randomised algorithms such as for example Simulated Annealing. The implementation is based on source-to-source transformation (STS). Using a recently developed prototype for STS for CHR, we could implement probabilistic CHR in a concise way with a few lines of code in less than one hour

Structure Learning of Quantum Embeddings

The representation of data is of paramount importance for machine learning methods. Kernel methods are used to enrich the feature representation, allowing better generalization. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding. We propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimization procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act. Since combinatorial optimization is computationally expensive, we have introduced a criterion based on the exponential concentration of kernel matrix coefficients around the mean to immediately discard an arbitrarily large portion of solutions that are believed to perform poorly. Contrary to the gradient-based optimization (e.g. trainable quantum kernels), our approach is not affected by the barren plateau by construction. We have used both artificial and real-world datasets to demonstrate the increased performance of our approach with respect to randomly generated PQC. We have also compared the effect of different optimization algorithms, including greedy local search, simulated annealing, and genetic algorithms, showing that the algorithm choice largely affects the result

Linear Embedding for a Quantitative Comparison of Language Expressiveness

Abstract We introduce the notion of linear embedding which refines Shapiro's notion of embedding by recasting it in a linear-space based semantics setting. We use this notion to compare the expressiveness of a class of languages that employ asynchronous communication primitives a la Linda. The adoption of a linear semantics in which the observables of a language are linear operators (matrices) representing the programs transition graphs allows us to give quantitative estimates of the different expressive power of languages, thus improving previous results in the field