Secure Computation
Currently deployed cryptography mainly deals with protecting confidential data at rest and during transit. But when data needs to be processed, then it must be decrypted, making it vulnerable to attacks.
We therefore look at how to overcome this issue by advancing research in secure multi-party computation and fully homomorphic encryption. Both concepts provide a provably secure way to process data under encryption and therefore are a key enabler for collaborative and outsourced computation on highly sensitive data.
The research of our group in this area is primarily focused on improving the efficiency of encrypted computing to enable building privacy-preserving applications at scale, for example, for secure distributed database analytics, which is required in a number of use cases such as enforcing anti-money laundering policies.
Selected Publications
- Hiroki Okada, Rachel Player, Simon Pohmann, Christian Weinert: Towards Practical Doubly-Efficient Private Information Retrieval. In FC 2024.
- Hiroki Okada, Rachel Player, Simon Pohmann: Homomorphic Polynomial Evaluation Using Galois Structure and Applications to BFV Bootstrapping. In ASIACRYPT (6) 2023.
- Anamaria Costache, Benjamin R. Curtis, Erin Hales, Sean Murphy, Tabitha Ogilvie, Rachel Player: On the Precision Loss in Approximate Homomorphic Encryption. In SAC 2023.