New Techniques Bringing Security to Consumer Data Analysis
Consumer-data stockpiles, comprising personal information from browsing or search histories, to credit-card purchases, to social-media interactions and presence, can be extremely valuable assets for businesses. The data in these stockpiles can be analyzed in innumerable ways to increase understanding of consumer habits, understanding which in turn can be used to significantly improve business practices like targeted advertising. However, the more this data gets used, the greater the risk that it can be traced back to individuals, which can be an extremely unwelcome invasion of privacy. Now, a new mathematical technique developed by researchers at Cornell University may allow for the sharing of larger personal data sets without compromising individual privacy. Check out the article from MIT's Technology Review for more details on the ways mathematicians are working to increase the privacy and security of big data analysis.