Meet CBI faculty host Elaine Shi working with Fellow Lisa
Dec 13, 2024
What is the focus of your research?
My research focuses on applied cryptography and security. In particular, my research has enabled the theory-to-practice transition for various cryptographic techniques. For example, I have developed privacy-preserving algorithms that are now widely deployed by companies such as Signal, Google, and JP Morgan.
What about this fellowship program drew your attention?
I was impressed by how well the fellowship is structured to foster success among its fellows. The fellows enjoy substantial research freedom, they are matched with an industry mentor, which facilitates their connections to industry, and they benefit from CMU's outstanding research community. Overall, I believe that the CBI fellowship is an excellent opportunity for young researchers to work on problems they find impactful.
What will the work supported by the fellowship focus on?
Very broadly, our work focuses on advancing the state-of-the-art in secure multi-party computation (MPC). MPC is a fascinating subfield of cryptography–it enables multiple mutually distrusting parties to collaboratively compute a function over their private inputs in a way that everything except for the final output is kept secret. It has numerous applications, particularly at the intersection of MPC and AI.
Consider, for example, a scenario where several hospitals seek to jointly train a machine learning model on their collective datasets. Clearly, the hospitals cannot simply share the sensitive patient data with each other due to privacy concerns. However, with MPC, they would be able to perform the training in an "encrypted" fashion, without revealing the data to each other.
Why is this topic exciting?
Within the realm of MPC, our focus will be on making it practical for large-scale distributed systems. While a lot of research on MPC is focused on scenarios with a fixed small number of parties, I believe that our research will help unlock even more exciting applications, especially at the intersection of cryptography/machine learning and Internet of Things (IoT). For instance, it would allow to train machine learning models on private data gathered from customer IoT devices without having direct access to that data.
Can you introduce Lisa?
Prior to her postdoc appointment, Elisaweta Masserova (Lisa) obtained her Ph.D. from CMU under the supervision of Bryan Parno and Vipul Goyal. She is broadly interested in applied cryptography, blockchains, and secure multi-party computation. I first met Masserova when she participated in my class on foundations of distributed consensus and blockchains, and we have collaborated on a few projects since then. Our first joint work, which discusses blockchain-based fair exchange, has been recently accepted to FC'25, and we are very excited to continue our research collaboration.
Masserova's dissertation topic was "Distributed Cryptography as a Service," where she touched upon the topic of MPC within the context of large-scale distributed systems. Specifically, she explored the YOSO (You Only Speak Once) paradigm, which is tailored to large-scale stateless environments such as blockchains. I am very excited to dive deeper into the intersection of MPC and distributed systems together with Masserova.