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This crate could be highly valuable to the Data Privacy world. If we could teach our systems to flag data that contains sensitive content, (e.g.: PII, NPPI, PCI), we can use Privacy by Design to implement guardrails to avoid privacy breeches or misuse of data.
A suggested enhancement for such an application would be to calculate the chance of a data string belonging to a classification.
Example
// returns 0 to 100pubfnmatch_classification(data_to_validate:String,category:String) -> u8{let chance = 0;/* calculating ... */
chance
}let chance = NaiveBayesClassifier::match_classification("ssn: 003-43-7621","NPPI");println!("This data is {} likely to be in the NPPI category.", chance);
The text was updated successfully, but these errors were encountered:
This crate could be highly valuable to the Data Privacy world. If we could teach our systems to flag data that contains sensitive content, (e.g.: PII, NPPI, PCI), we can use Privacy by Design to implement guardrails to avoid privacy breeches or misuse of data.
A suggested enhancement for such an application would be to calculate the chance of a data string belonging to a classification.
Example
The text was updated successfully, but these errors were encountered: