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 100
pub fn match_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);
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