Our research focuses on secure multiparty computation (MPC) which is a collection of techniques, that allows us to compute on encrypted data without the data ever leaving the encrypted domain. Only selected results will be revealed as clear text.
MPC allows us to solve many of the privacy problems posed by the big data economy. Big data is often also sensitive data which, for instance, contains personally identifiable information (PII) that needs to be protected. On the one hand, it is predicted that data-driven analytics for decision support or automated decision making will be a tremendous growth factor in the years to come. On the other hand, in many cases, the use of PII threatens the privacy of data subjects. MPC allows mitigating this problem by never revealing the actual data to anyone, only the aggregate result, which can be designed to not contain any personal data.
MPC can also be used to facilitate more collaboration and solve novel problems. Often the quality of the result of an algorithm becomes much better when more data is available. However, often the data is held by different companies, which are not willing to share their data with their potential competitors. Such conflicts between a desire to collaborate and problems with sharing the needed data occur in a number of industrial applications like benchmarking, supply chain optimisation and machine learning. With MPC a computation can be done over the entire dataset without revealing the data to the other companies. MPC allows automating and making rapid such collaborative decision-making, opening up completely novel venues for collaboration.
Another aspect of security concerns exploits based on errors in programming or use of a system. We are performing research in semantics, logic and programming focusing on formal methods that support reasoning about systems to be implemented. We are also researching suitable methodologies for development and maintenance.