Using multi-party computation to solve real-world problems
Multi-party computation (MPC) is a field with a long history, but it has typically faced many hurdles to widespread adoption beyond academic communities. Common challenges include finding effective and efficient ways to tailor encryption techniques and tools to solve practical problems.
And this is just the beginning of what’s possible. This technology can help advance valuable research in a wide array of fields that require organizations to work together without revealing anything about individuals represented in the data. For example:
Public policy – if a government implements new wellness initiatives in public schools (e.g. better lunch options and physical education curriculums), what are the long-term health outcomes for impacted students?
Diversity and inclusion – when industries create new programs to close gender and racial pay gaps, how does this impact compensation across companies by demographic?
Healthcare – when a new preventative drug is prescribed to patients across the country, does it reduce the incidence of disease?
Car safety standards – when auto manufacturers add more advanced safety features to vehicles, does it coincide with a decrease in reported car accidents?
Private Join and Compute keeps individual information safe while allowing organizations to accurately compute and draw useful insights from aggregate statistics. By sharing the technology more widely, we hope this expands the use cases for secure computing. To learn more about the research and methodology behind Private Join and Compute, read the full paper and access the open source code and documentation. We’re excited to see how other organizations will advance MPC and cryptography to answer important questions while upholding individual privacy.
Product Manager – Nirdhar Khazanie Software Engineers – Mihaela Ion, Benjamin Kreuter, Erhan Nergiz, Quan Nguyen, and Karn Seth
Research Scientist – Mariana Raykova