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?

  • $\begingroup$ 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

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.

  • $\begingroup$ Thanks for laying out like this. Yeah.. what I was looking for was de-identification. $\endgroup$
    – Krishnaa
    Aug 24 '15 at 9:22

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