2018 has been the year of privacy. News of Facebook’s exposure of tens of millions of user accounts to data firm Cambridge Analytica broke in March — a scandal that was only compounded by recent news that the tech giant shared even more private data through hidden agreements with other companies. Then in May, the European Union’s General Data Protection Regulation, the world’s most stringent privacy law, came into effect. By the end of the year, even Apple’s and Microsoft’s CEOs were calling for new national privacy standards in the United States.
It’s not just a coincidence that privacy issues dominated 2018. These events are symptoms of larger, profound shifts in the world of data privacy and security that have major implications for how organizations think about and manage both.
So what, exactly, is changing?
Put simply, privacy and security are converging, thanks to the rise of big data and machine learning. What was once an abstract concept designed to protect expectations about our own data is now becoming more concrete, and more critical — on par with the threat of adversaries accessing our data without authorization.
More specifically, the threat of unauthorized access to our data used to pose the biggest danger to our digital selves — that was a world in which we worried about intruders attempting to get at data we wanted private. And it was a world in which privacy and security were largely separate functions, where privacy took a backseat to the more tangible concerns over security. Today, however, the biggest risk to our privacy and our security has become the threat of unintended inferences, due to the power of increasingly widespreadmachine learning techniques. Once we generate data, anyone who possesses enough of it can be a threat, posing new dangers to both our privacy and our security.
These inferences may, for example, threaten our anonymity — like when a group of researchers used machine learning techniques to identify authorship of written text based simply on patterns in language. (Similar techniques have been used to identify software developers based simply on the code they’ve written.)
These inferences might reveal information about our political leanings — like when researchers used the prevalence of certain types of cars in Google’s Street View image database to determine local political affiliations.
Or these inferences might also indicate intimate details about our health — like when researchers used online search history to detect neurodegenerative disorders such asAlzheimer’s.
So what does a world look like when privacy and security are focused on preventing the same harms?
To start with, privacy will no longer be the merely immaterial or political concept it once was. Instead, privacy will begin to have substantial impacts on businesses’ bottom lines — something we began to see in 2018. Facebook, for example, lost a whopping $119 billion in market capitalization in the wake of the Cambridge Analytica scandal because of concerns over privacy. Polls show that consumers are increasingly concerned about privacy issues. And governments around the world are reacting with new privacy legislation of their own.