What Is Secure Multiparty Computation?
Secure MPC allows us to collaboratively analyze data for the public good without revealing private information
Data can be used to 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, data sharing is even constrained or prevented by legal, ethical, or privacy restrictions.
To harness the power of big data while maintaining privacy protection, researchers at Boston University’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering are leveraging a cryptography technology called secure multiparty computation (MPC), which allows collaborative data analysis without revealing private data in the process.
Through MPC protocol, parties enter their data, which is then split into separate pieces and masked with other random numbers; the encoded data pieces are 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.
Researchers led by Hariri Institute director Azer Bestavros have been 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 BU’s Initiative on Cities (IOC), Boston-area companies submitted payroll data that was securely collected and redistributed for analysis, revealing that the city’s women make 77 cents for every dollar a man makes.
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