Insights from combined data are important for better healthcare, smarter business operations, and more effective policy. However, in practice, you cannot simply combine datasets due to privacy and data laws and the increasing risks of data breaches. So how do you ensure that you can still extract new insights from combined data? Linksight, a TNO spin-off, is developing a method to gain insights from encrypted data using TTT-AI. CEO Martine van de Gaar discusses their innovation.
This article was previously published in TTT Magazine #10.
How can you learn something about something if you have never seen it before? That is exactly what Linksight makes possible, explains Martine. “Our technological breakthrough is that we have converted a cryptographic method into workable protocols, supported by data collaboration governance. The result is a product that allows end users to extract insights from combined, encrypted data. The data remains at the source, but the encrypted information can be used for calculations. So you get the insights, but not the data itself.” This creates many new opportunities for companies. “You have control over your data yourself,” says Martine. “You decide who is allowed to do something with your data, or a part of your data. You can determine this for each separate collaboration.”
New analyses
Through Linksight, organizations have access to insights from various datasets without seeing the data. “Every piece of data is encrypted,” Martine explains. “So you don’t know what it is, but you can perform calculations with it, so you can extract the insights. In fact, you can combine datasets without sharing sensitive data. This makes new analyses possible.” The technology can, for example, be useful in tackling the waiting list problem in mental healthcare. “Those lists can be compared with each other to see how many unique individuals are on the lists. Perhaps a person has registered twice or with multiple agencies. You can then find that out without seeing the individual details.”
Product introductions
Most of Linksight’s clients are in healthcare, a sector where a lot of work is done with sensitive data. “We conduct research regarding effectiveness, such as process improvements or product innovations,” says Martine. “Consider, for example, eye drop glasses that could potentially reduce the need for home care after cataract surgery. You then have to investigate whether that is indeed the case: can people use them independently? And are enough drops being administered? For this, you need data from various parties: from the hospital, you need to know who they operated; from pharmacists, to whom they dispensed the glasses; from home care, to whom they provide care, and so on. So, you have different parties with data that you cannot simply lump together, even though you want those insights to improve care. We can calculate these kinds of things to see if such a product introduction will indeed help organizations take a step forward.”
Self-sovereignty
With this technology, Linksight meets a major need of organizations in 2026: self-sovereignty. In other words, self-determination over data. “We allow our customers to increase efficiency without having to compromise on privacy,” says Martine. “With the rise of AI, more and more models are becoming available. Our solution makes it possible to train all those AI models securely. This also makes it possible to use sensitive data for AI models. I also envision our technology becoming completely mainstream and creating a different mindset. That we will start finding the throwing together of datasets just as strange as smoking in a café. I really want to make that shift in mindset possible.”