Information leakage traditionally has been defined to occur when uncertainty about secret data is reduced. This uncertainty-based approach is inadequate for measuring information flow when an attacker is making assumptions about secret inputs and these assumptions might be incorrect; such attacker beliefs are an unavoidable aspect of any satisfactory definition of leakage. To reason about information flow based on beliefs, a model is developed that describes how attacker beliefs change due to the attacker’s observation of the execution of a probabilistic (or deterministic) program. The model leads to a new metric for quantitative information flow that measures accuracy rather than uncertainty of beliefs. 1.

This paper presents an abstract interpretation framework for parallelizing compilers. Within this framework, symbolic analysis is used to solve various flow analysis problems in a unified way. Symbolic analysis also serves as a basis for code generation optimizations and a tool for derivation of computation cost estimates. A loop scheduling strategy that utilizes symbolic timing information is also presented. 1 Introduction Empirical results indicate that existing parallelizing compilers cause insignificant improvements on the performance of many real application programs [9, 5]. The speedups obtained by manual transformation of these applications [9] show the potential for significantly advancing parallelizing compiler technology. The poor performance of current restructuring compilers can be attributed to two causes: imprecise analysis and inappropriate performance-wise transformations. The causes are not completely independent; namely, imprecise information results in inappropriate...

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Lambda--Upsilon--Omega ( \Upsilon\Omega ) is a research tool designed to assist the average case analysis of some well defined classes of algorithms and data structures. This cookbook consists of an informal introduction to the system together with eighteen examples of programmes that are automatically analyzed. Amongst the applications treated here, we find: addition chains, quantitative concurrency analysis of simple systems, symbolic manipulation algorithms such as formal differentiation, simplification and rewriting systems, as well as combinatorial models including various tree and permutation statistics and functional graphs with applications to integer factorisation.

To reason about information flow, a new model is developed that describes how attacker beliefs change due to the attacker’s observation of the execution of a probabilistic (or deterministic) program. The model enables compositional reasoning about information flow from attacks involving sequences of interactions. The model also supports a new metric for quantitative information flow that measures accuracy of an attacker’s beliefs. Applying this new metric reveals inadequacies of traditional information flow metrics, which are based on reduction of uncertainty. However, the new metric is sufficiently general that it can be instantiated to measure either accuracy or uncertainty. The new metric can also be used to reason about misinformation; deterministic programs are shown to be incapable of producing misinformation. Additionally, programs in which nondeterministic choices are made by insiders, who collude with attackers, can be analyzed. 1

A complete and decidable Hoare-style calculus for iteration-free probabilistic sequential programs is presented using a state logic with truthfunctional propositional (not arithmetical) connectives. 1

We study the problem of automatically analyzing the worst-case resource usage of procedures with several arguments. Existing automatic analyses based on amortization, or sized types bound the resource usage or result size of such a procedure by a sum of unary functions of the sizes of the arguments. In this paper we generalize this to arbitrary multivariate polynomial functions thus allowing bounds of the form mn which had to be grossly overestimated by m 2 + n 2 before. Our framework even encompasses bounds like ∑ i,j≤n mimj where the mi are the sizes of the entries of a list of length n. This allows us for the first time to derive useful resource bounds for operations on matrices that are represented as lists of lists and to considerably improve bounds on other super-linear operations on lists such as longest common subsequence and removal of duplicates from lists of lists. Furthermore, resource bounds are now closed under composition which improves accuracy of the analysis of composed programs when some or all of the components exhibit super-linear resource or size behavior. The analysis is based on a novel multivariate amortized resource analysis. We present it in form of a type system for a simple firstorder functional language with lists and trees, prove soundness, and describe automatic type inference based on linear programming. We have experimentally validated the automatic analysis on a wide range of examples from functional programming with lists and trees. The obtained bounds were compared with actual resource consumption. All bounds were asymptotically tight, and the constants were close or even identical to the optimal ones.

Abstract. A complete and decidable propositional logic for reasoning about states of probabilistic sequential programs is presented. The state logic is then used to obtain a sound Hoare-style calculus for basic probabilistic sequential programs. The Hoare calculus presented herein is the first probabilistic Hoare calculus with a complete and decidable state logic that has truth-functional propositional (not arithmetical) connectives. The models of the state logic are obtained exogenously by attaching sub-probability measures to valuations over memory cells. In order to achieve complete and recursive axiomatization of the state logic, the probabilities are taken in arbitrary real closed fields. 1

Autonomous mobile programs (AMPs) offer a novel decentralised load management technology where periodic use is made of cost models to decide where to execute in a network. In this paper we demonstrate how sequential programs can be automatically converted into AMPs. The AMPs are generated by an automatic continuation cost analyser that replaces iterations with Costed Autonomous Mobility Skeletons (CAMS), that encapsulate autonomous mobility. The CAMS cost model uses an entirely novel continuation cost semantics to predict both the cost of the current iteration and the continuation cost of the remainder of the program. We show that CAMS convey significant performance advantages, e.g. reducing execution time by up to 53%; that the continuation cost models are consistent with the existing AMP cost models; and that the overheads of collecting and utilising the continuation costs are relatively small. We discuss example AMPs generated by the analyser and demonstrate that they have very similar performance to hand-costed CAMS programs.

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