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In the following we discuss how the fingerprint is processed according to each field classification.
The client computes the fingerprint for the template according to the field classification.
In order to circumvent these problems, this paper proposes extensions to the DEPSPACE field classification to prevent that the privacy of data and users are breached by attackers.
On the other hand, the protocol to read/remove a tuple works as follows: The client computes the fingerprint for the template according to the field classification.
Our proposal for new field classification employs these cryptographic mechanisms aiming to mitigate the security problems for applications that use the tuple space programming paradigm.
It increases the security provided by DepSpace, mainly privacy, by proposing new field classification that uses robust cryptographic schemes and, consequently, reduces (and even eliminates) the need for public or comparable fields.
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Before presenting the new field classifications, let us introduce another very important characteristic of DEPSPACE.
All decision rules were based upon the opinion of the expert panel; these rules then provided the basis by which all field classifications were made.
Statistics Canada attempted to derive a match measure by linking the categories of the 1980 Standard Occupational Classification to the 1991 Census Major Field of Study classification (Lathe 1996).
This constitutes a broad impact of this paper, which goes beyond the innovative bioinformatics methodology and obvious implications in the field of domain classification and evolution.
The client computes the fingerprint according to the fields classification.
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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