Maximum data protection and use
The VEIL.AI developers set out to create a solution that would maximise both data protection and usability, while minimising the time and computational capacity needed to process data. Traditional methods usually require data to be generalised to such an extent that the potential for the subsequent use of data sets decreases considerably. In addition, traditional methods are poorly suited to anonymising dynamic, continuously updated masses of data.
VEIL.AI uses artificial intelligence to speed up the computationally heavy processes required for de-identification.
VEIL.AI offers solutions to companies and major partnerships
Many large companies collect a great deal of data about their clients, but make very little use of it. Analyses of such extensive data sets through methods of machine learning can provide companies with significant value when, for example, developing new types of services.
However, many companies see the use of their data resources as too challenging or risky, particularly in light of the requirements of the EU’s General Data Protection Regulation (GDPR). The anonymisation tools offered by VEIL.AI support companies in utilising their valuable data.
Research projects must often use data sets from several sources. To do this, the relevant organisations must usually either share their data sets with all project partners or select someone to merge the data. With VEIL.AI, multi-partner projects become easier, as raw data no longer needs to be shared in order to be merged.
VEIL.AI is ideal for editing many types of individual data
VEIL.AI has been developed at the University of Helsinki’s Institute for Molecular Medicine Finland (FIMM) under the leadership of Janna Saarela and Timo Miettinen. The current team also includes three developers. The responsibility for business development lies with technology veteran Tuomo Pentikäinen and Petri Junttila of Helsinki Innovation Services.
The VEIL.AI developers have extensive experience in working with patient samples and biobank resources.
“Such research projects have required new tools because no suitable ones have been available. That’s why organisations concentrating on medical research, such as FIMM, are several years ahead of the rest of the world in terms of data protection,” says Tuomo Pentikäinen.
Despite the team’s specialisation in medicine, VEIL.AI can process data sets for research and development in all fields using individual-level data.
The commercial potential of VEIL.AI is currently being explored with New Business from Research Ideas funding from Business Finland. The team is now focusing on the further development of the application and on pilot projects using various research data sets.
”FIMM and their veil.ai team are working on very novel concepts. We are preparing a joint research project, where we investigate the use of synthetic data, as it is expected to help in some of the most burning problems prevalent in data-intensive HealthTech development. The target of the research is to significantly reduce the lead time of R&D, to reduce or even remove the data breach risks and to improve the quality of data,” says Professor Henning Langberg from the Copenhagen Healthtech cluster and the University of Copenhagen.