Transform Data Into Variant Twins
Gartner identifies ”Digital Twins” - digital representations of real-world entities or systems - as a top-ten strategic trend for 2019. However, Gartner also highlights the “privacy paradox” between customer’s concerns over protecting the privacy of their “Digital Twin” and their desire for more personalized products, services and experiences.
The graphic to the right from the Georgetown Law Technology Review article Re-Identification of “Anonymized Data” shows that data exists along a spectrum of identifiability with privacy and utility being on opposite ends of this spectrum - with maximum utility (and identifiability) at the top and maximum privacy at the bottom. The article highlights that traditional privacy technologies fail to reconcile conflicts between data utility and privacy in two respects – they fail to effectively protect privacy while also failing to maximize the utility of data.
Anonos BigPrivacy dynamically transforms identifying Digital Twin personal data into non-identifying Variant Twin ®️ versions of data to reconcile the ”privacy paradox” and ease the conflict between data privacy and utility. By dynamically managing the risk of re-identification of both direct and indirect identifiers, BigPrivacy enforces privacy respectful data use in a way that is auditable and demonstrable. BigPrivacy supports GDPR compliant Legitimate Interest processing by dynamically enforcing technical and organizational safeguards (including GDPR certified Pseudonymisation) to mitigate the risk to data subjects to enable privacy respectful personalized products, services and experiences.
The difference between Anonos’ BigPrivacy-enabled Variant Twins and other privacy technologies (including differential privacy, anonymisation and static tokenisation) is that Variant Twins support the multiple-use, dataset set combination and decentralized processing that is necessary for successful Analytics, AI and ML.