When collecting and analysing big data, security and privacy are of utmost importance, because data can be commercially valuable or highly privacy-sensitive. To address the concerns being raised in terms of privacy is one of the challenges in big data processing.
To preserve privacy, it is essential to follow the Privacy by Design principles when designing systems. These principles suggest using scientific tools such as anonymisation techniques, access control, and advanced cryptographic tools to control the amount of information leakage without hampering the healthy function of the system.
The goal of DDS research is to develop efficient and scalable algorithms for sensitive data, using state-of-the-art anonymisation techniques and cryptographic tools. Research topics are computing with encrypted data, multi-party computation, homomorphic encryption and light weight cryptography.
Our focus is on privacy-preserving solutions that consider trade-offs between the provided level of privacy, the utility and the overhead of the deployed privacy-preserving tools. In addition, privacy-preserving solutions are highly dependent on the application domain, which means that the algorithms and techniques have to be custom-tailored for domain requirements.