Project Details

Layman's description

When you sign up for a new messaging service, you might want to find out who in your contact list also uses the service. Similarly, when considering if your pay is fair, you may be interested in the average salary of your peers. These tasks sound useful, but not at the expense of your privacy. This is where Multi-party Computation (MPC) comes into play. MPC is a branch of cryptography that allows multiple parties to compute a result together without revealing their individual inputs. Applications range from secure voting systems to complex analyses of private data, including collaborative medical research where patient confidentiality is of high priority, financial services where sensitive transaction data needs protection, and joint data analysis by competing businesses without revealing proprietary information. 

Although the ideas behind MPC have been around since the 1980s, recent advancements are making it increasingly practical for industry use. MPC can be applied in various scenarios. Handling the potential corruption of participants - whether one or many, static or proactive - is another consideration. The duration of protocol execution, whether for a single session or extended periods, also influences the development and application of MPC. These diverse requirements drive ongoing innovations in the field.

MPC relies on cryptographic techniques such as digital signatures and encryption, which are based on complex mathematical problems. However, some of these problems may be vulnerable to future quantum computers. Other challenges include ensuring security in the universal composability (UC) model, where protocols must remain secure regardless of the environment in which they operate.

In our project, we aim to develop and analyze new MPC protocols that are resistant to quantum computing threats and offer practical performance.
StatusActive
Effective start/end date15/01/202414/01/2027

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.