A computational study of integer programming algorithms based on Barvinok's rational functions

Loera, Jesús A. De; Haws, David; Hemmecke, Raymond; Huggins, Peter; Yoshida, Ruriko

doi:10.1016/j.disopt.2005.04.001

Cited by 19 publications

(17 citation statements)

References 10 publications

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“…For example, if A is represented in linear arithmetic, this task can be performed efficiently using Barvinok's algorithm [8]. The Lattice Point Enumeration Tool (LattE) [43] provides an implementation of this algorithm.…”

Section: Rant: a Randomized Algorithm For Quantitative Information Flmentioning

confidence: 99%

“…Hence for each low output value l, the set of ghost input values h, such that l and h are related by F reach , overapproximates the preimage of l with respect to the original program. The size of these approximated preimages can be determined using tools for counting models, e.g., we use LattE [43] for dealing with linear arithmetic assertions. In our example, we obtain 5 as an upper bound for the size of the preimage of each value of l.…”

Section: Estimating Shannon Entropymentioning

confidence: 99%

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Automation of Quantitative Information-Flow Analysis

Köpf

Rybalchenko

2013

Lecture Notes in Computer Science

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Abstract. Quantitative information-flow analysis (QIF) is an emerging technique for establishing information-theoretic confidentiality properties. Automation of QIF is an important step towards ensuring its practical applicability, since manual reasoning about program security has been shown to be a tedious and expensive task. In this chapter we describe a approximation and randomization techniques to bear on the challenge of sufficiently precise, yet efficient computation of quantitative information flow properties.

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Section: Rant: a Randomized Algorithm For Quantitative Information Flmentioning

confidence: 99%

Section: Estimating Shannon Entropymentioning

confidence: 99%

Automation of Quantitative Information-Flow Analysis

Köpf

Rybalchenko

2013

Lecture Notes in Computer Science

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show abstract

“…Hence for each low output value l, the set of ghost input values h, such that l and h are related by F reach , over-approximates the preimage of l with respect to the original program. The size of these approximated preimages can be determined using tools for counting models, e.g., we use LattE [34] for dealing with linear arithmetic assertions. In our example, we obtain 5 as an upper bound for the size of the preimage of each value of l.…”

Section: A Dealing With Loopsmentioning

confidence: 99%

Approximation and Randomization for Quantitative Information-Flow Analysis

Köpf

Rybalchenko

2010

2010 23rd IEEE Computer Security Foundations Symposium

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Abstract-Quantitative information-flow analysis (QIF) is an emerging technique for establishing information-theoretic confidentiality properties. Automation of QIF is an important step towards ensuring its practical applicability, since manual reasoning about program security has been shown to be a tedious and expensive task. Existing automated techniques for QIF fall short of providing full coverage of all program executions, especially in the presence of unbounded loops and data structures, which are notoriously difficult to analyze automatically. In this paper we propose a blend of approximation and randomization techniques to bear on the challenge of sufficiently precise, yet efficient computation of quantitative information flow properties. Our approach relies on a sampling method to enumerate large or unbounded secret spaces, and applies both static and dynamic program analysis techniques to deliver necessary over-and underapproximations of information-theoretic characteristics.

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“…The base manipulations of polyhedra are done using the Parma Polyhedra Library [18]. Size calculations are done using the LattE lattice point counter [19]. LattE is also used for the integer linear programming problem involved in the abstract forget operation.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

“…Support for revision requires that we maintain both underand over-approximations of the querier's belief, whereas [17] deals only with over-approximation. We have developed an implementation of our approach based on Parma [18] and LattE [19], which we present in Section VII along with some experimental measurements of its performance. We find that while the performance of Probabilistic Scheme degrades significantly as the input space grows, our implementation scales much better, and can be orders of magnitude faster.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Enforcement of Knowledge-Based Security Policies

Mardziel

Magill

Hicks

et al. 2011

2011 IEEE 24th Computer Security Foundations Symposium

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Abstract-This paper explores the idea of knowledge-based security policies, which are used to decide whether to answer a query over secret data based on an estimation of the querier's (possibly increased) knowledge given the result. Limiting knowledge is the goal of existing information release policies that employ mechanisms such as noising, anonymization, and redaction. Knowledge-based policies are more general: they increase flexibility by not fixing the means to restrict information flow. We enforce a knowledge-based policy by explicitly tracking a model of a querier's belief about secret data, represented as a probability distribution. We then deny any query that could increase knowledge above a given threshold. We implement query analysis and belief tracking via abstract interpretation using a novel domain we call probabilistic polyhedra, whose design permits trading off precision with performance while ensuring estimates of a querier's knowledge are sound. Experiments with our implementation show that several useful queries can be handled efficiently, and performance scales far better than would more standard implementations of probabilistic computation based on sampling.

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A computational study of integer programming algorithms based on Barvinok's rational functions

Cited by 19 publications

References 10 publications

Automation of Quantitative Information-Flow Analysis

Automation of Quantitative Information-Flow Analysis

Approximation and Randomization for Quantitative Information-Flow Analysis

Dynamic Enforcement of Knowledge-Based Security Policies

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