I would like to know what are the best practices for anonymizing datasets? Ideally I should be able to get the original data back after performing analysis on the anonymized dataset. Should I be using some encryption functions? Hashing maybe?
$\begingroup$
$\endgroup$
-
$\begingroup$ Not really clear what you want since if you make it generally reversible, it has not been anonymized . You probably have a user/role model and a consumption model in mind, but you need to explicitly describe this in the question. As well as taking Franck's answer into consideration. $\endgroup$ – Mike Wise Aug 1 '15 at 20:14
$\begingroup$
$\endgroup$
You should make the difference between the following concepts:
- encryption = encoding information in such a way that only authorized parties can read it.
- data anonymization = removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.
- de-identification = preventing a person’s identity from being connected with information, while preserving identifying information which could only be re-linked by a trusted party in certain situations (unlike data anonymization which aims to be irreversible).
Your choice depends on your use case.
answered Aug 1 '15 at 17:47
-
$\begingroup$ Thanks for laying out like this. Yeah.. what I was looking for was de-identification. $\endgroup$ – Krishnaa Aug 24 '15 at 9:22
