6,680 research outputs found
Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
In privacy-preserving data publishing, approaches using Value Generalization
Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs
play a key role in the utility of published datasets as they dictate how the
anonymization of the data occurs. For categorical attributes, it is imperative
to preserve the semantics of the original data in order to achieve a higher
utility. Despite this, semantics have not being formally considered in the
specification of VGHs. Moreover, there are no methods that allow the users to
assess the quality of their VGH. In this paper, we propose a measurement
scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in
terms of semantic consistency and taxonomic organization, with the aim of
producing higher-quality anonymizations. We demonstrate, through a case study,
how our evaluation scheme can be used to compare the quality of multiple VGHs
and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical
Databases Conference 2014 (Ibiza, Spain
Guide to using Evidence in Higher Education
This Guide to Using Evidence has been designed to, to support and encourage students and students’ association and union staff to actively engage with data and evidence. It offers an accessible introduction to a range of key ideas and concepts and a range of activities which allow readers to develop their own thinking and confidence in key areas.
The ambition of its authors, QAA Scotland and the students who reviewed early drafts, is that students and students’ association and union staff will reach for this resource as they prepare for committees, devise new campaigns, deliver services, and do all of the other things they do to enhance students’ experiences and outcomes. Underpinning all of this is a belief that students themselves, the institutions they are working with, and the sector as a whole, are better served when students are, and are seen to be, agents in the ‘data landscape’, not just subjects of it. Engaging with this Guide will help students and students’ association and union staff to develop that sense of agency in themselves and foster it in others.
This Guide is a product of a student-led project coordinated by QAA Scotland as part of the Evidence for Enhancement Theme (2017-20)
Ranking authors using fractional counting of citations : an axiomatic approach
This paper analyzes from an axiomatic point of view a recent proposal for counting citations: the value of a citation given by a paper is inversely proportional to the total number of papers it cites. This way of fractionally counting citations was suggested as a possible way to normalize citation counts between fields of research having different citation cultures. It belongs to the “citing-side” approach to normalization. We focus on the properties characterizing this way of counting citations when it comes to ranking authors. Our analysis is conducted within a formal framework that is more complex but also more realistic than the one usually adopted in most axiomatic analyses of this kind
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