Advanced analytics, machine learning and other data science techniques are powerful tools for transformation. However, because “big data” entails large and complex data sets, the privacy risks associated with such endeavors are incredibly high.
Not only are organizations legally obligated to protect personal identifiable information (PII) from external threats, how companies use such data is also coming under increased scrutiny. As a result, preserving privacy of users has become a key requirement for many web-scale analytics and reporting applications.
Organizations looking to enable the sharing, processing or analysis of personal data without compromising privacy are increasingly adopting privacy preserving data analytics (PPDA) strategies. Rather than a specific tool or technology, PPDA represents a privacy-first approach to delivering data analytics.
Though PPDA first and foremost requires an effective, mathematically robust definition of privacy, it also relies on a combination of data protection systems and technologies – most of which result in data anonymization – to secure data. The following is an overview of some of those approaches.