A cryptography technology called secure multiparty computation (MPC) allows collaborative data analysis without revealing private data in the process.

Data can help analyze complex problems, shine a light on new solutions, or even resolve otherwise unanswerable questions. But when it comes to using data for the public good, such as finding new drug targets for cancer or understanding how ride-sharing apps can influence traffic congestion, there is often societal tension between data sharing and data protection.

In many cases, legal, ethical, or privacy restrictions constrain or even prevent data sharing, but MPC could change that.

Through MPC protocol, parties enter their data, which then splits into separate pieces and masks with other random numbers; the encoded data pieces get sent to multiple servers, assuring data privacy. Organizations can, for example, input financial, personal, or patient data for comparison and analysis without ever receiving or seeing other parties’ data.

Azer Bestavros, director of Boston University’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering, has led researchers in developing new applications for MPC in areas such as healthcare, transportation, higher education, public policy, and business.

In 2015, the team used MPC software to analyze Boston’s gender wage gap. In collaboration with Boston University’s Initiative on Cities (IOC), Boston-area companies submitted payroll data that researchers securely collected and redistributed for analysis, revealing that the city’s women make 77 cents for every dollar a man makes.

In this video, researchers explain what MPC is and how it could make the world of data analysis more secure.

Source: Jessica Colarossi for Boston University

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