1,304 research outputs found
Provenance Views for Module Privacy
Scientific workflow systems increasingly store provenance information about
the module executions used to produce a data item, as well as the parameter
settings and intermediate data items passed between module executions. However,
authors/owners of workflows may wish to keep some of this information
confidential. In particular, a module may be proprietary, and users should not
be able to infer its behavior by seeing mappings between all data inputs and
outputs. The problem we address in this paper is the following: Given a
workflow, abstractly modeled by a relation R, a privacy requirement \Gamma and
costs associated with data. The owner of the workflow decides which data
(attributes) to hide, and provides the user with a view R' which is the
projection of R over attributes which have not been hidden. The goal is to
minimize the cost of hidden data while guaranteeing that individual modules are
\Gamma -private. We call this the "secureview" problem. We formally define the
problem, study its complexity, and offer algorithmic solutions
Automatic vs Manual Provenance Abstractions: Mind the Gap
In recent years the need to simplify or to hide sensitive information in
provenance has given way to research on provenance abstraction. In the context
of scientific workflows, existing research provides techniques to semi
automatically create abstractions of a given workflow description, which is in
turn used as filters over the workflow's provenance traces. An alternative
approach that is commonly adopted by scientists is to build workflows with
abstractions embedded into the workflow's design, such as using sub-workflows.
This paper reports on the comparison of manual versus semi-automated approaches
in a context where result abstractions are used to filter report-worthy results
of computational scientific analyses. Specifically; we take a real-world
workflow containing user-created design abstractions and compare these with
abstractions created by ZOOM UserViews and Workflow Summaries systems. Our
comparison shows that semi-automatic and manual approaches largely overlap from
a process perspective, meanwhile, there is a dramatic mismatch in terms of data
artefacts retained in an abstracted account of derivation. We discuss reasons
and suggest future research directions.Comment: Preprint accepted to the 2016 workshop on the Theory and Applications
of Provenance, TAPP 201
DEEP: a provenance-aware executable document system
The concept of executable documents is attracting growing interest from both academics and publishers since it is a promising technology for the dissemination of scientific results. Provenance is a kind of metadata that provides a rich description of the derivation history of data products starting from their original sources. It has been used in many different e-Science domains and has shown great potential in enabling reproducibility of scientific results. However, while both executable documents and provenance are aimed at enhancing the dissemination of scientific results, little has been done to explore the integration of both techniques. In this paper, we introduce the design and development of DEEP, an executable document environment that generates scientific results dynamically and interactively, and also records the provenance for these results in the document. In this system, provenance is exposed to users via an interface that provides them with an alternative way of navigating the executable document. In addition, we make use of the provenance to offer a document rollback facility to users and help to manage the system's dynamic resources
AiiDA: Automated Interactive Infrastructure and Database for Computational Science
Computational science has seen in the last decades a spectacular rise in the
scope, breadth, and depth of its efforts. Notwithstanding this prevalence and
impact, it is often still performed using the renaissance model of individual
artisans gathered in a workshop, under the guidance of an established
practitioner. Great benefits could follow instead from adopting concepts and
tools coming from computer science to manage, preserve, and share these
computational efforts. We illustrate here our paradigm sustaining such vision,
based around the four pillars of Automation, Data, Environment, and Sharing. We
then discuss its implementation in the open-source AiiDA platform
(http://www.aiida.net), that has been tuned first to the demands of
computational materials science. AiiDA's design is based on directed acyclic
graphs to track the provenance of data and calculations, and ensure
preservation and searchability. Remote computational resources are managed
transparently, and automation is coupled with data storage to ensure
reproducibility. Last, complex sequences of calculations can be encoded into
scientific workflows. We believe that AiiDA's design and its sharing
capabilities will encourage the creation of social ecosystems to disseminate
codes, data, and scientific workflows.Comment: 30 pages, 7 figure
Privacy Issues in Scientific Workflow Provenance
A scientific workflow often deals with proprietary modules as well as private or confidential data, such as health or medical information. Hence providing exact answers to provenance queries over all executions of the workflow may reveal private information. In this paper we first study the potential privacy issues in a scientific workflow – module privacy, data privacy, and provenance privacy, and frame several natural questions: (i) can we formally analyze module, data or provenance privacy giving provable privacy guarantees for an unlimited/bounded number of provenance queries? (ii) how can we answer provenance queries, providing as much information as possible to the user while still guaranteeing the required privacy? Then we look at module privacy in detail and propose a formal model from our recent work in [11]. Finally we point to several directions for future work
Abstracting PROV provenance graphs:A validity-preserving approach
Data provenance is a structured form of metadata designed to record the activities and datasets involved in data production, as well as their dependency relationships. The PROV data model, released by the W3C in 2013, defines a schema and constraints that together provide a structural and semantic foundation for provenance. This enables the interoperable exchange of provenance between data producers and consumers. When the provenance content is sensitive and subject to disclosure restrictions, however, a way of hiding parts of the provenance in a principled way before communicating it to certain parties is required. In this paper we present a provenance abstraction operator that achieves this goal. It maps a graphical representation of a PROV document PG1 to a new abstract version PG2, ensuring that (i) PG2 is a valid PROV graph, and (ii) the dependencies that appear in PG2 are justified by those that appear in PG1. These two properties ensure that further abstraction of abstract PROV graphs is possible. A guiding principle of the work is that of minimum damage: the resultant graph is altered as little as possible, while ensuring that the two properties are maintained. The operator developed is implemented as part of a user tool, described in a separate paper, that lets owners of sensitive provenance information control the abstraction by specifying an abstraction policy.</p
DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud
The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific
DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud
The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific
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