Cryptography for social good: Meet Lisa Masserova

Lisa Masserova is thrilled to be a part of the 2024 CBI fellowship cohort at CMU. She previously earned her Ph.D. in computer science, advised by Bryan Parno and Vipul Goyal. During her Ph.D., she worked in the field of applied cryptography, exploring topics such as blockchains, multi-party computation, and metadata-private messaging. Masserova is broadly interested in using cryptography to improve the security-related aspects of existing systems and enable new, exciting use cases for social good.

Her long-term goals:

Masserova's long-term goal is to contribute to the development of secure, scalable, and practical cryptographic systems that empower individuals and organizations to collaborate without compromising privacy or security. She envisions a future where privacy-preserving technologies become standard across industries, including finance, healthcare, and public policy.

Academically, Masserova hopes to one day establish a research group that pushes the boundaries of applied cryptography and secure computation. She aims to foster collaborations that bridge the gap between theoretical advancements and real-world implementations. Additionally, she aspires to mentor the next generation of researchers and innovators, inspiring them to tackle important challenges in privacy and security.

How does she envision the CBI Postdoc Fellows program helping her reach these goals?

The CBI fellowship provides Masserova with an exceptional foundation for launching her academic career. It enables her to freely pursue her research interests while building connections in both academia and industry. This network will be instrumental as she refines her skills and expands the impact of her work. The mentorship provided through the program has already proven invaluable, inspiring her to explore innovative directions and broaden the scope of her research.

What is the biggest challenge she hopes to tackle?

Masserova hopes to address the challenges of ensuring privacy and security amidst the ever-growing volumes of data, the rapid adoption of machine learning, and the increasing complexity of distributed systems. As more organizations rely on collaborative data analysis and machine learning, safeguarding sensitive information while enabling meaningful computations becomes critical. She aims to develop scalable and efficient cryptographic protocols for secure multi-party computation that can handle the vast amounts of data generated today.

Additionally, Masserova is focused on integrating privacy-preserving techniques into distributed systems without compromising performance or usability. By tackling these issues, she seeks to create frameworks that empower secure, privacy-conscious innovation in data-driven fields.

What are some obstacles she will face?

Addressing these challenges involves overcoming several complex obstacles. One of the most prominent is the tension between privacy and scalability. Current cryptographic techniques, such as secure multi-party computation and zero-knowledge proofs, often struggle to handle the vast amounts of data generated in modern applications. Masserova is driven to find ways to improve the efficiency of these techniques, making them practical for real-world deployment.

In the context of machine learning, privacy can often conflict with other critical properties, such as fairness. For example, demonstrating that a model is fair may require revealing certain aspects of its structure or data, which could compromise privacy. Masserova believes cryptography can resolve this tension. Techniques like zero-knowledge proofs allow fairness to be proven without disclosing additional information, preserving both privacy and fairness simultaneously. However, tailoring such techniques to new machine learning models is a challenge that requires developing customized cryptographic protocols to handle the unique structures and complexities of these models efficiently.

Finally, fostering trust and adoption in both industry and academia is essential but difficult. Masserova is committed to finding ways to encourage organizations to adopt secure computation methods, despite potential trade-offs in cost, performance, or complexity. Bridging the gap between theoretical advancements and real-world needs will require extensive collaboration, education, and demonstration of the tangible benefits of these technologies.

By addressing these questions, Masserova aims to contribute to building a more secure and privacy-conscious digital ecosystem.