On October 1st the research project “AI-Based Privacy-Preserving Big Data Sharing for Market Research” (in short “Anonymous Big Data”) has started. It is funded by FFG and aims to systematically validate the feasibility of using deep recurrent neural network architectures to generate synthetic sequential raw data that preserve individual privacy and, at the same time, retain enough information to be used for market research. In this project Vienna University of Economics and Business collaborates with Mostly AI Solutions MP GmbH, George Labs GmbH and Statistik Austria.
Generative deep neural networks have recently become a highly active research field within artificial intelligence, with impressive demonstrations for synthetic image generation. The combination and application of these developed methods to sequential personal data could provide a viable solution to the utilization problem of the growing asset of personal data, while safeguarding the privacy of individuals. Together with our consortium partners, we are going to design and set up a virtual data lab that will allow us to systematically investigate the conditions under which a variety of deep generative models are able to derive synthetic replicas that capture structure and correlations, while protecting individual-level privacy.