The purpose is to render the data record less identifying and therefore lower customer or patient objections to its use.
Data in this form is suitable for extensive analytics and processing.
Now that Research In Motion/Blackberry Limited has pretty much plummeted we’ve gone back to that archaic means of exchanging numbers.
But not to fret, you guys don’t really talk on the phone anyway.
The choice of which data fields are to be pseudonymized is partly subjective, but should include all fields that are highly selective, NHS number (in the UK) for example.
Less selective fields, such as Birth Date or Postal Code are often also included because they are usually available from other sources and therefore make a record easier to identify.
Protecting statistically useful pseudonymized data from re-identification requires: ), where all person-related data that could allow backtracking has been purged.
Pseudonymization is an issue in, for example, patient-related data that has to be passed on securely between clinical centers.
Pseudonymization is a procedure by which the most identifying fields within a data record are replaced by one or more artificial identifiers, or pseudonyms.
There can be a single pseudonym for a collection of replaced fields or a pseudonym per replaced field.
Not to worry, that’s not necessarily a reflection on either of you. No one wants to really show someone off who may potentially be a no-one in a matter of days. In fact you would have most likely met each other’s friends but in the most casual haphazard ways, you are after all kinda sorta friends.
There’s also a silent wager between you two in which no one wants to be the first to ruin a perfectly good thing.
The application of pseudonymization to e-health intends to preserve the patient's privacy and data confidentiality.