It is common knowledge these days that you exist all over the internet.  Each site you view, app you use, and company you deal with tracks you in different ways, building databases of information which help them develop more effective (and profitable) services.  While this data is often protected by privacy policies, these policies generally allow data to be shared with anyone, given certain steps are taken to anonymize the data.  However, as we mentioned in a previous post, Harvard Researcher Latanya Sweeney has recently shown that data can never truly be anonymized, but can be pieced together using other publicly available information to “fill in the blanks”.

This is unfortunate since mining big data can be incredibly useful, not just for maximizing profits, but for measuring larger social trends and analyzing regional health concerns.  So how can we analyze data without sacrificing privacy, when traditional anonymization does not cut it? One solution is through harnessing differential privacy.  With differential privacy, whenever data is transferred between parties, it is randomly altered in ways which do not change how the database behaves statistically, but provides a mathematical limit on the probability of identifying any one entry.   That limit is the database’s privacy score.

Currently, differential privacy is facing a number of mathematical hurdles including developing more efficient algorithms which require less computing time and ensuring that the random alterations to the database cannot be sniffed out.  Given the fractured state of American privacy law, even once these technical hurdles are surmounted, it will be difficult to have differential privacy become the norm.  Were it to succeed, this tool would be invaluable to wide-scale social research, with very promising implications in the fields of medicine, sociology, economics, etc.

One of the many fronts in technology’s war against personal privacy comes from the refinement of facial recognition software.  Like the eye scanners in the movie “Minority Report”, these new programs are able to scan and identify people’s faces on the fly, and can be simply implemented into common security systems or personal devices.  While this tech has tons of useful and beneficial applications (such as blurring out the faces on the people captured in Google Maps’ “street view”) the privacy implications are terrifying.

What is worse is the efficiency with which these new programs can identify you.  Some of the more sophisticated of them can identify a face in profile, from many angles, even when partially obscured.  A trench coat and sunglasses will no longer be enough to keep you out of the spotlight, and in response a new movement has sprung up trying to develop ways of foiling facial recognition technology.  The website cvdazzle.com attempts to meld camouflage and fashion by developing provocative hair and makeup styles, to confuse computer (and human) onlookers.  The Japanese National Institute of Informatics disregards style entirely with its new anti-recognition goggles.  The goggles are covered in LEDs which blast infra-read light to wash out computer images but without blinding the people around them.  However, it is likely that further advances in the technology will be able to see through these disguises, leading to an arms-race of sorts between the hiders and seekers.  As this technology becomes more prevalent, it is likely that we will need a more legal solution to this surveillance problem.