English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

When a sample frequency table is published, disclosure risk arises when someindividuals can be identified on the basis of their values in certainattributes in the table called key variables, and then their values in otherattributes may be inferred, and their privacy is violated. On the basis of thesample to be released, and possibly some partial knowledge of the wholepopulation, an agency which considers releasing the sample, has to estimate thedisclosure risk. Risk arises from non-empty sample cells which represent smallpopulation cells and from population uniques in particular. Therefore riskestimation requires assessing how many of the relevant population cells arelikely to be small. Various methods have been proposed for this task, and wepresent a method in which estimation of a population cell frequency is based onsmoothing using a local neighborhood of this cell, that is, cells havingsimilar or close values in all attributes. We provide some preliminary resultsand experiments with this method. Comparisons are made to two other methods: 1.a log-linear models approach in which inference on a given cell is based on a``neighborhood'' of cells determined by the log-linear model. Suchneighborhoods have one or some common attributes with the cell in question, butsome other attributes may differ significantly. 2 The Argus method in whichinference on a given cell is based only on the sample frequency in the specificcell, on the sample design and on some known marginal distributions of thepopulation, without learning from any type of ``neighborhood'' of the givencell, nor from any model which uses the structure of the table.

A smoothing model for sample disclosure risk estimation