Giant stockpiles of personal data, whether Web browsing logs, credit-card purchases, or the information shared through social networks, are becoming increasingly valuable assets for businesses. Such data can be analyzed to determine trends that guide business strategy, or sold to other businesses for a tidy profit. But as your personal data is analyzed and handed around, the risk increases that it could be traced back to you, presenting an unwelcome invasion of privacy.
A new mathematical technique developed at Cornell University could offer a way for large data sets of personal data to be shared and analyzed while guaranteeing that no individual's privacy will be compromised.
