Improving control of logic programs by using functional logic languages

Hanus, Michael

doi:10.1007/3-540-55844-6_124

Cited by 11 publications

(19 citation statements)

References 29 publications

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“…λ J differs from existing constraint functional languages [9,10,15,25] in two key ways: 1) λ J restricts its constraint language to quantifier-free constraints involving boolean and arithmetic expressions over primitive values and records and 2) λ J supports default values for logic variables to facilitate reasoning about nondeterminism. λ J 's restrictions on the constraint language allow execution to rely on an off-the-shelf SMT solver.…”

Section: The λ J Language and Semanticsmentioning

confidence: 99%

A language for automatically enforcing privacy policies

Yang

Yessenov

Solar-Lezama

2012

Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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It is becoming increasingly important for applications to protect sensitive data. With current techniques, the programmer bears the burden of ensuring that the application's behavior adheres to policies about where sensitive values may flow. Unfortunately, privacy policies are difficult to manage because their global nature requires coordinated reasoning and enforcement. To address this problem, we describe a programming model that makes the system responsible for ensuring adherence to privacy policies. The programming model has two components: 1) core programs describing functionality independent of privacy concerns and 2) declarative, decentralized policies controlling how sensitive values are disclosed. Each sensitive value encapsulates multiple views; policies describe which views are allowed based on the output context. The system is responsible for automatically ensuring that outputs are consistent with the policies. We have implemented this programming model in a new functional constraint language named Jeeves. In Jeeves, sensitive values are introduced as symbolic variables and policies correspond to constraints that are resolved at output channels. We have implemented Jeeves as a Scala library using an SMT solver as a model finder. In this paper we describe the dynamic and static semantics of Jeeves and the properties about policy enforcement that the semantics guarantees. We also describe our experience implementing a conference management system and a social network.

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Section: The λ J Language and Semanticsmentioning

confidence: 99%

A language for automatically enforcing privacy policies

Yang

Yessenov

Solar-Lezama

2012

Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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“…Narrowing is a combination of term reduction as in functional programming and (non-deterministic) variable instantiation as in logic programming. It exploits the presence of functions without transforming them into predicates which yields a more efficient operational behavior (e.g., see [17,20]). …”

Section: Functional Logic Programming and Currymentioning

confidence: 99%

Functional Logic Design Patterns

Antoy

Hanus

2002

Functional and Logic Programming

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Abstract. We introduce a handful of software design patterns for functional logic languages. Following usual approaches, for each pattern we propose a name and we describe its intent, applicability, structure, consequences, etc. Our patterns deal with data type construction, identifier declarations, mappings, search, nondeterminism and other fundamental aspects of the design and implementation of programs. We present some problems and we show fragments of programs that solve these problems using our patterns. The programming language of our examples is Curry. The complete programs are available on-line.

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“…[Red85] and [Han92]. So is there any excuse for my not coming up with a core model, capturing the abstract properties needed to formulate correctness/speedup theorems etc?…”

Section: Integrating the Functional And Logical Modelmentioning

confidence: 99%

Sharing of Computations

Amtoft¹

1993

DPB

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Danish summaryDenne rapport er en revideret udgave af min afhandling af samme navn, som i juni 1993 blev accepteret til forsvar af PhD-graden i datalogi ved Aarhus Universitet. MotivationI de senereår har man arbejdet meget med at udvikle vaerktøjer til at gøre programmer mere effektive. Af teknikker kan naevnes memoisering [Kho90]; udfold/fold transformationer [PP91b]; graf-baseret implementation af "lazy" evaluering [Jon87] og partiel evaluering [JSS89]. At disse metoder forbedrer effektiviteten skyldes at nogle beregninger deles, så de kun skal gøresén gang. Imidlertid er forbindelsen mellem teknikkerne ikke klart forstået, og det er heller ikke klart hvor stor effektivitetsforbedring (speedup) de kan forårsage. Ydermere giver anvendelse af teknikkerne udfold/fold og partiel evaluering risiko for ødelaeggelse af termineringsegenskaber.Den existerende litteratur inden for området vidner om mangel på en model for program udførelse/transformering der er abstrakt nok. Behandlingen har vaeret for afhaengig af det konkrete sprog/system, og derfor er de essentielle begreber ofte druknet i detaljer.Formålet med denne afhandling er at praesentere en model (faktisk to, nemligén for et funktionelt sprog ogén for et logisk sprog) som jeg tror/håber vil hjaelpe til med at isolere de karakteristiske traek ved optimeringsteknikker der er baserede på at beregninger deles.Modellen er basered på en transitionssemantik i Plotkin-stil [Plo81]. Grunden til at en transitionssemantik foretraekkes frem for en denotationel semantik er at førstnaevnte bedre fanger at udfoldning/foldning er operationelle begreber.Hovedidéen, at bruge mange-niveau transitionssystemer, er som føl-ger:• Det oprindelige program (kildeprogrammet) er repraesenteret som regler på niveau 0.• At udføre kildeprogrammet modelleres af (en sekvens af) transitioner på niveau 1, hvor man på niveau 1 "har adgang til" regler på niveau 0. 1• At transformere kildeprogrammet (at foretage "symbolsk evaluering") bliver ligeledes modelleret af transitioner på niveau 1. Resultatet af transformationen, målprogrammet, vil blive repraesenteret som regler på niveau 1.• At udføre målprogrammet modelleres af transitioner på niveau 2, hvor man på niveau 2 har adgang til regler på niveau 1.At beregninger deles afspejles i at en regel på niveau 1, så snart den er udledt, kan bruges mange gange på niveau 2 -hver anvendelse repraesenterer en genvej i beregningsprocessen. Man kan taenke på kildeprogrammet som en samling axiomer i en teori T 0 ; så kan man opfatte målprogrammet som enten en samling teoremer i T 0 eller som en samling axiomer i en ny teori T 1 , som man kan forvente vil vaere mere effektiv end T 0 -cf. [Gru87].I vores model kan korrekthed (løst sagt) udtrykkes som følger: hvis der er en transition på niveau 2 fra en konfiguration C til en anden konfiguration C , skal der også vaere en transition på niveau 1 fra C til (noget "aekvivalent med") C (dette er partiel korrekthed); og hvis der fra en konfiguration C udgår en uendelig kaede af transitioner på niveau 2, skal der og...

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Improving control of logic programs by using functional logic languages

Cited by 11 publications

References 29 publications

A language for automatically enforcing privacy policies

A language for automatically enforcing privacy policies

Functional Logic Design Patterns

Sharing of Computations

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