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guarantee that a category of analytical queries can be answered correctly, within a quantifiable approximation that specifies the data utility, using the transformed data instead of the original ones. .
transform the data into an anonymous version with a quantifiable privacy guarantee - i.e., the probability that the malicious attack fails; guarantee that a category of analytical queries can be answered correctly, within a quantifiable approximation that specifies the data utility, using the transformed data instead of the original ones.
In general, a framework for protecting the corporate privacy in data mining outsourcing must guarantee: (1) to the data owner the possibility to query its data in outsourcing (2) to the server provider to answer the queries of the data owner with an encrypted result that does not allow to infer any knowledge (3) to the data owner to recover the query results within a quantifiable approximation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com