4,385 research outputs found
Privacy-aware Linked Widgets
The European General Data Protection Regulation (GDPR) brings
new challenges for companies, who must demonstrate that their
systems and business processes comply with usage constraints
specified by data subjects. However, due to the lack of standards,
tools, and best practices, many organizations struggle to adapt their
infrastructure and processes to ensure and demonstrate that all
data processing is in compliance with users' given consent. The
SPECIAL EU H2020 project has developed vocabularies that can
formally describe data subjects' given consent as well as methods
that use this description to automatically determine whether
processing of the data according to a given policy is compliant
with the given consent. Whereas this makes it possible to determine
whether processing was compliant or not, integration of the
approach into existing line of business applications and ex-ante
compliance checking remains an open challenge. In this short paper,
we demonstrate how the SPECIAL consent and compliance framework
can be integrated into Linked Widgets, a mashup platform, in
order to support privacy-aware ad-hoc integration of personal data.
The resulting environment makes it possible to create data integration
and processing workflows out of components that inherently
respect usage policies of the data that is being processed and are
able to demonstrate compliance. We provide an overview of the
necessary meta data and orchestration towards a privacy-aware
linked data mashup platform that automatically respects subjects'
given consents. The evaluation results show the potential of our
approach for ex-ante usage policy compliance checking within the
Linked Widgets Platforms and beyond
Big Data and Analytics in the Age of the GDPR
The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks.We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects
Machine Understandable Policies and GDPR Compliance Checking
The European General Data Protection Regulation (GDPR) calls for technical
and organizational measures to support its implementation. Towards this end,
the SPECIAL H2020 project aims to provide a set of tools that can be used by
data controllers and processors to automatically check if personal data
processing and sharing complies with the obligations set forth in the GDPR. The
primary contributions of the project include: (i) a policy language that can be
used to express consent, business policies, and regulatory obligations; and
(ii) two different approaches to automated compliance checking that can be used
to demonstrate that data processing performed by data controllers / processors
complies with consent provided by data subjects, and business processes comply
with regulatory obligations set forth in the GDPR
Data Privacy Vocabularies and Controls: Semantic Web for Transparency and Privacy
Managing Privacy and understanding the handling of personal data has turned into a fundamental right-at least for Europeans-since May 25th with the coming into force of the General Data Protection Regulation. Yet, whereas many different tools by different vendors promise companies to guarantee their compliance to GDPR in terms of consent management and keeping track of the personal data they handle in their processes, interoperability between such tools as well uniform user facing interfaces will be needed to enable true transparency, user-configurable and -manageable privacy policies and data portability (as also implicitly promised by GDPR). We argue that such interoperability can be enabled by agreed upon vocabularies and Linked Data
Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures
Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives
Controlled vocabularies and semantics in systems biology
The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments
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