Household finance studies aim at understanding market participation and asset allocation decisions of private households . Unfortunately, the required data to run analyses is hard to obtain due to data protection regulations preventing financial institutions to share such widely distributed data and due to privacy concerns of individuals that limit the expressiveness of representative surveys. Luckily, secure multi-party computation (SMPC) offers a way to privately compute on sensitive data that is distributed among multiple institutions such that researchers in the end only obtain the desired aggregate statistics [2,3].
The goal of this thesis is to efficiently adapt existing SMPC technology for privacy-preserving finance analytics. For this, it is necessary to design, implement, and evaluate a framework for performing privacy-preserving statistics on financial data at a country-level scale. This includes modeling suitable database schemes, privately joining and aggregating data across multiple databases, devising efficient statistical calculations in our SMPC tools ABY  or MOTION , and evaluating the resulting performance on large-scale synthetic data sets.
- Good programming skills in Java and basic programming skills in C/C++ and SQL
- At least basic knowledge of cryptography
- High motivation + ability to work independently
- Knowledge of the English language, Git, LaTeX, etc. goes without saying
-  John Y. Campbell. (opens in new tab). In Journal of Finance, 2006. Household Finance
-  Daniel Demmler, Thomas Schneider and Michael Zohner. (opens in new tab). In NDSS, 2015. ABY – A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
-  Lennart Braun, Daniel Demmler, Thomas Schneider and Oleksandr Tkachenko. . In ePrint, 2020. MOTION – A Framework for Mixed-Protocol Multi-Party Computation