Only a handful of experts exist around the world who are qualified to manually de-identify data. This is because de-identification is a complex and challenging field that requires highly specific knowledge. Simply removing the names and other types of direct identifiers from a dataset is insufficient to achieve de-identification. The data will also contain other indirect or quasi-identifiers, such as age, date of birth and zip code that, when combined, can be used to positively identify an individual.
As would-be attackers get smarter, data de-identification strategies become more sophisticated. It can be hard to keep up. Learning the basics helps you be part of the discussion.
Join us for De-identification 201, Fundamentals of Data De-identification.
This whitepaper covers classic de-identification techniques like record suppression, cell suppression, sub-sampling and aggregation as well as the pros and cons of Safe Harbour and Expert De-identification strategies.
Make sure to read De-identification 101, 301 and 401 to get the full picture.
