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.

Source: Why the collision of big data and privacy will require a new realpolitik — Tech News and Analysis

Matt Devost

Matt Devost

Matthew G. Devost is the CEO & Co-Founder of OODA LLC. Matt is a technologist, entrepreneur, and international security expert specializing in counterterrorism, critical infrastructure protection, intelligence, risk management and cyber-security issues. Matt co-founded the cyber security consultancy FusionX from 2010-2017. Matt was President & CEO of the Terrorism Research Center/Total Intel from 1996-2009. For a full bio, please see