Quantified self devices create unique data signatures for each user
This fascinating story on GigaOm discusses the implications of big data and personal privacy. What was uniquely interesting was this quote from CIA Chief Technology Officer Gus Hunt in describing how your FitBit health tracker collects enough data about how you move to uniquely identify you with 100 percent accuracy. Hunt’s comments from a public presentation in New York highlight the issue:
“‘You guys know the Fitbit, right? It’s just a simple three-axis accelerometer. We like these things because they don’t have any – well, I won’t go into that [laughter]. What happens is, they discovered that just simply by looking at the data what they can find out is with pretty good accuracy what your gender is, whether you’re tall or you’re short, whether you’re heavy or light, but what’s really most intriguing is that you can be 100 percent guaranteed to be identified by simply your gait – how you walk.’”
I wrote a personal blog post last week entitled “I am Big Data and so are you” that discussed how individuals might be empowered to use their own big data archives to augment their lives and workflow. Additionally, there have been numerous examples about extracting uniquely identifying data from anonymous data sets such as search and browser history. However, this is the first story I’ve seen that mentions unique identifiers based upon a quantified self device like a FitBit.