Zachary Amos covers a topic of note:
Data analytics tools allow users to quickly and thoroughly analyze large quantities of material, accelerating important processes. However, individuals must ensure to maintain privacy while doing so, especially when working with personally identifiable information (PII).
One possibility is to perform de-identification methods that remove pertinent details. However, evidence has suggested such options are not as effective as once believed. People may still be able to extract enough information from what remains to identify particular parties.
Read on to learn a bit more about the impetus behind differential privacy and a few of the techniques you can use to get there. The real trick with differential privacy is adding the right kind of noise not to distort the distribution of the data, while still not allowing an end user to unearth enough information to identify a specific individual.