An Authorisation Scenario for S-OGSA

The Semantic Grid initiative aims to exploit knowledge in the Grid to increase the automation, interoperability and flexibility of Grid middleware and applications. To bring a principled approach to developing Semantic Grid Systems, and to outline their core capabilities and behaviors, we have devised a reference Semantic Grid Architecture called S-OGSA. We present the implementation of an S-OGSA observant semantically-enabled Grid authorization scenario, which demonstrates two aspects: 1) the roles of different middleware components, be them semantic or non-semantic, and 2) the utility of explicit semantics for undertaking an essential activity in the Grid: resource access control

S-OGSA as a Reference Architecture for OntoGrid and for the Semantic Grid

The Grid aims to support secure, flexible and coordinated resource sharing through providing a middleware platform for advanced distributing computing. Consequently, the Grid’s infrastructural machinery aims to allow collections of any kind of resources—computing, storage, data sets, digital libraries, scientific instruments, people, etc—to easily form Virtual Organisations (VOs) that cross organisational boundaries in order to work together to solve a problem. A Grid depends on understanding the available resources, their capabilities, how to assemble them and how to best exploit them. Thus Grid middleware and the Grid applications they support thrive on the metadata that describes resources in all their forms, the VOs, the policies that drive then and so on, together with the knowledge to apply that metadata intelligently

Managing semantic Grid metadata in S-OGSA

Grid resources such as data, services, and equipment, are increasingly being annotated with descriptive metadata that facilitates their discovery and their use in the context of Virtual Organizations (VO). Making such growing body of metadata explicit and available to Grid services is key to the success of the VO paradigm. In this paper we present a model and management architecture for Semantic Bindings, i.e., firstclass Grid entities that encapsulate metadata on the Grid and make it available through predictable access patterns. The model is at the core of the S-OGSA reference architecture for the Semantic Grid

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

Grid metadata management: requirements and architecture

Metadata annotations of grid resources can potentially be used for a number of purposes, including accurate resource allocation to jobs, discovery of services, and precise retrieval of information resources. In order to realize this potential on a large scale, various aspects of metadata must be managed. These include uniform and secure access to distributed and independently maintained metadata repositories, as well as management of metadata lifecycle. In this paper we analyze these issues and present a service-oriented architecture for metadata management, called S-OGSA, that addresses them in a systematic way

Requirements and services for metadata management

Knowledge-intensive applications pose new challenges to metadata management, including distribution, access control, uniformity of access, and evolution in time. The authors identify general requirements for metadata management and describe a simple model and service that focuses on RDF metadata to address these requirements

A Linked Data Approach to Sharing Workflows and Workflow Results

A bioinformatics analysis pipeline is often highly elaborate, due to the inherent complexity of biological systems and the variety and size of datasets. A digital equivalent of the ‘Materials and Methods’ section in wet laboratory publications would be highly beneficial to bioinformatics, for evaluating evidence and examining data across related experiments, while introducing the potential to find associated resources and integrate them as data and services. We present initial steps towards preserving bioinformatics ‘materials and methods’ by exploiting the workflow paradigm for capturing the design of a data analysis pipeline, and RDF to link the workflow, its component services, run-time provenance, and a personalized biological interpretation of the results. An example shows the reproduction of the unique graph of an analysis procedure, its results, provenance, and personal interpretation of a text mining experiment. It links data from Taverna, myExperiment.org, BioCatalogue.org, and ConceptWiki.org. The approach is relatively ‘light-weight’ and unobtrusive to bioinformatics users

Architectural Patterns for the Semantic Grid

The Semantic Grid reference architecture, S-OGSA, includes semantic provisioning services that are able to produce semantic annotations of Grid resources, and semantically aware Gridservices that are able to exploit those annotations in various ways. In this paper we describe the dynamic aspects of S-OGSA by presenting the typical patterns of interaction among these services. A use case for a Grid meta-scheduling service is used to illustrate how the patterns are applied in practice

Fair and Private Data Preprocessing through Microaggregation

Copyright \ua9 2023 held by the owner/author(s).Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsible Machine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while becoming uninformative of the sensitive information. The intrinsic connection between privacy and fairness implies that modifications performed to guarantee one of these goals, may have an effect on the other, e.g., hiding a sensitive attribute from a classification algorithm might prevent a biased decision rule having such attribute as a criterion. This work resides at the intersection of algorithmic fairness and privacy. We show how the two goals are compatible, and may be simultaneously achieved, with a small loss in predictive performance. Our results are competitive with both state-of-the-art fairness correcting algorithms and hybrid privacy-fairness methods. Experiments were performed on three widely used benchmark datasets: Adult Income, COMPAS, and German Credit