by the guest editors Rudolf Mayer (SBA Research) and Thijs Veugen (TNO and CWI)
Many branches of the economy and society are increasingly dependent on data-driven predictions, decision support and autonomous decision-making by systems. These systems often depend on the availability of large amounts of data to learn from, which may include details about individuals, or otherwise sensitive information. Disclosure of the individuals represented by this data must be avoided for ethical reasons or regulatory requirements, which have been tightened in recent years, e.g. by the introduction of the EU’s General Data Protection Regulation (GDPR). This means that the use of data is restricted, making sharing, combining, and analysing data problematic. Privacy-preserving computation tries to bridge this gap: to find a way to leverage data while preserving the privacy of individuals.