Description
Secure Multi-Party Computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. SMPC is widely used in privacy-preserving machine learning, finance, and data collaboration where confidentiality is essential; however, its communication and computational overheads limit the application scenarios to small workloads.
Trusted hardware is an emerging computing paradigm which involves specialized components designed to provide a secure and isolated environment for sensitive computations and data. These components ensure confidentiality and integrity against even privileged system software like an operating system or hypervisor.
This internship is to explore how we can leverage trusted hardware to accelerate SMPC by offloading sensitive computations to hardware-protected enclaves, and to significantly reduce the need for complex cryptographic protocols and communication overhead between parties, even for large workloads. Ideally, this project will lead to a publication at a top academic venue.
Responsibilities
* You will be expected to get up to speed with various SMPC protocols and trusted hardware options.
* You will design a novel system that has a software-hardware co-design, best leveraging the advantages of SMPC and trusted hardware.
* You will implement a working prototype and be involved in writing an academic paper related to the project.
Qualifications
* Student enrolled in a PhD program in Computer Science/Engineering.
* Strong programming skills in Python and C.
* Experience in designing, implementing and evaluating distributed systems is a big plus.
* A strong publication record is a big plus.
* Language skill: English.
Location: Stuttgart (Germany)