Data-driven analytics for decision support or automated decision making is currently enjoying tremendous growth, with clear implications to society in terms of security and privacy. On one hand, computations must be done securely for them to be relied upon, but at the same time they must be performed without revealing sensitive information and threatening the privacy of data subjects. The more society trusts computer systems to perform important tasks, the more relevant their security becomes.

Our research is comprised of constructive aspects of attacking and defending networked computer systems of different types. We develop techniques mainly based in cryptographic mechanisms and their application, but we also have an interest in other topics such as software security and electronic voting. We collaborate frequently with the Cryptography and Security Group at the Department of Computer Science and other work packages at DIGIT.

We focus specifically on three research fronts:

• Cryptographic Engineering is the application of algorithmic techniques, theoretical tools, and rigorous experimentation to solve efficiency and security problems for the deployment of cryptography. In a world where strong cryptography mediates a substantial part of human activity, its security is critical not only for protecting sensitive information but also societal values like privacy and freedom of speech. The study of techniques to translate the mathematical strength of cryptography to practice, where imperfect software stacks and computing platforms are commonplace, is a challenging but fundamental goal. Some of the classic research problems in Cryptographic Engineering are developing efficient software implementations of cryptography, protecting those implementations against unintended leakage through side channels, and lately developing implementations verifiable for correctness and absence of leakage.

• Multi-party computation (MPC) is a collection of techniques to compute on encrypted data without the data ever leaving the encrypted domain, such that only selected results will be revealed as clear text. MPC allows us to solve many of the privacy problems posed by processing large volumes of data which, for instance, contains personally identifiable information (PII) that needs to be protected. 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, for example when 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.

• Security of real-world systems is fundamental to study and improve, such that critical systems can be depended upon when performing their tasks. Developing a holistic view of a computer system from a security point of view is challenging, since modern systems are comprised of many building blocks as part of the attack surface, ranging from the processors executing software to the communication channels needed for interaction. We have experience in performing security analysis of various computer systems, including mobile applications for banking and digital identity solutions, or dedicated systems for electronic voting that rely on cryptography to conduct electronic elections.