Research shows that t raditional de-identification of data is not working as expected. Curre nt research shows that a data set with 15 demographic attributes can make 9 9.98% of the state of Massachusetts unique. In smaller areas it is, of course, much less. Examples of re-identified data include Netfli x in 2008 and data leaked to the public, as well as home addresses of New York taxi drivers from an anonymous data set of individual trips in the city. This presentation will detail exactly what GDPR and CCPA requir e in data protection and the shortcomings of some current de-identification techniques. Additionally covered will be techniques to properly ident ify data, which include purposely fuzzing data so that data reported i s not actually accurate. The data would be "skewed" in a sta ndard way. Other options include encryption techniques where the data cannot be decrypted except by the owner.
\nAttendees will learn:
\n\n Why and how data is re-identified\n What GDPR and CCPA require\n Examples of re-identification\n Best solutions to properly de-identify data\n Companies that are doing a better job of de-identification of data\n\n DTSTART:20200325T083000 SUMMARY:Why "De-Identification" Will NOT Satisfy GDPR and CCPA Solutions DTEND:20200325T092959 LOCATION: See Description END:VEVENT END:VCALENDAR