Accessible and Scalable
Secure Multi-Party Computation

Researchers at Boston University, together with collaborators at several other institutions and organizations, are developing open-source libraries, frameworks, and systems that enable the implementation and deployment of applications that employ secure multi-party computation in accessible and scalable ways. Please contact us if you would like to learn more or are interested in collaborating.

Introductions to MPC

  • Watch this video (about 32 minutes) to learn more about MPC and our work.
  • Read this paper to learn about how we are using software solutions that support MPC to help government agencies and non-profit organizations.
  • Check the FAQ page to see if it already contains the answers to your questions about MPC; if not, email us directly.

Software Libraries and Frameworks

  • JavaScript Implementation of Federated Functionalities (JIFF).
    JavaScript library for building web-based applications that employ secure multi-party computation (MPC).
  • Web-MPC.
    JavaScript application for user-friendly privacy-preserving web-based data aggregation use cases.
  • Conclave Workflow Manager.
    Query compiler that automatically optimizes relational queries to be executed under MPC by factoring it into (1) scalable, local, cleartext processing workflows (using backends such as Apache Spark) and (2) isolated MPC workflows that utilize existing MPC backend frameworks.

Publications and Reports

Presentations and Talks

Current Collaborators

Faculty

Azer Bestavros
Boston University
Andrei Lapets
Boston University
Mayank Varia
Boston University
Ran Canetti
Boston University
Shannon Roberts
UMass

Researchers and Software Engineers

Frederick Jansen
Boston University
Lucy Qin
Boston University
Ben Getchell
Boston University
Nikolaj Volgushev
Alexandra Institute
Malte Schwarzkopf
MIT
Shreya Pandit
Boston University
Ira Globus-Harris
Boston University
Peter Flockhart
Boston University
San Tran
Boston University

Students

Kinan Dak Albab
Boston University
Rawane Issa
Boston University
Bassel El Mabsout
Boston University
Wyatt Howe
Boston University
Mina Michael
German University in Cairo
Marcella Hastings
University of Pennsylvania

Past Collaborators

Students

Justin Chen
Boston University
Mike Gajda
Boston University
Rose Kelly
UMass
Eric Dunton
Boston University
Kyle Holzinger
Boston University
Rachel Manzelli
Boston University
Jacqueline You
Boston University

Acknowledgments

This effort exists thanks to the support and cooperation of Boston University, including the Department of Computer Science, the Hariri Institute for Computing, the Software & Application Innovation Lab, the MACS project, the Mass Open Cloud, and the Initiative on Cities.

We also thank the City of Boston, the Boston Women's Workforce Council, and the Greater Boston Chamber of Commerce.

This work is partially supported by the National Science Foundation under Grants #1430145 (SCOPE), #1414119 (MACS), #1718135, and #1739000. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This work is also supported in part by the Honda Research Institutes.