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).
JavaScript Implementation of Garbled Gates (JIGG).
JavaScript library for building web-compatible instances of 2PC boolean circuit protocols that use garbled circuits.
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

Role-Based Ecosystem for the Design, Development, and Deployment of Secure Multi-Party Data Analytics Applications.
Andrei Lapets, Kinan Dak Albab, Rawane Issa, Lucy Qin, Mayank Varia, Azer Bestavros, and Frederick Jansen.
Proceedings of the IEEE Secure Development Conference (SecDev). September 2019.
From Usability to Secure Computing and Back Again.
Lucy Qin, Peter Flockhart, Andrei Lapets, Frederick Jansen, Kinan Dak Albab, Mayank Varia, Shannon Roberts, Ira Globus-Harris.
Proceedings of the 15th Symposium on Usable Privacy and Security (SOUPS). August 2019.
Conclave: Secure Multi-Party Computation on Big Data.
Nikolaj Volgushev, Malte Schwarzkopf, Ben Getchell, Mayank Varia, Andrei Lapets, Azer Bestavros.
Proceedings of the 14th European Conference on Computer Systems (EuroSys). March 2019.
Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities.
Andrei Lapets, Frederick Jansen, Kinan Dak Albab, Rawane Issa, Lucy Qin, Mayank Varia, and Azer Bestavros.
Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. June 2018.
Callisto: A Cryptographic Approach to Detecting Serial Perpetrators of Sexual Misconduct.
Anjana Rajan, Lucy Qin, David W. Archer, Dan Boneh, Tancrède Lepoint, Mayank Varia
Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. June 2018.
Federated Code Auditing and Delivery for MPC.
Frederick Jansen, Kinan Dak Albab, Andrei Lapets, and Mayank Varia.
Proceedings of SSS 2017: 19th Annual International Symposium on Stabilization, Safety, and Security of Distributed Systems. November 2017.
Scalable Secure Multi-Party Network Vulnerability Analysis via Symbolic Optimization.
Kinan Dak Albab, Rawane Issa, Andrei Lapets, Azer Bestavros, and Nikolaj Volgushev.
Proceedings of WTMC 2017: The 2nd International Workshop on Traffic Measurements for Cybersecurity. May 2017.
User-Centric Distributed Solutions for Privacy-Preserving Analytics.
Azer Bestavros, Andrei Lapets, and Mayank Varia.
Communications of the ACM, 60(2):37-39, 2017.
Secure MPC for Analytics Deployed as a Lightweight Web Application.
Andrei Lapets, Nikolaj Volgushev, Azer Bestavros, Frederick Jansen, and Mayank Varia.
Proceedings of 1st IEEE Cybersecurity Development Conference (SecDev). November 2016.
DEMO: Integrating MPC in Big Data Workflows.
Nikolaj Volgushev, Malte Schwarzkopf, Andrei Lapets, Mayank Varia, and Azer Bestavros.
Proceedings of CCS 2016: The 23rd ACM SIGSAC Conference on Computer and Communications Security. October 2016.
Programming Support for an Integrated Multi-Party Computation and MapReduce Infrastructure.
Nikolaj Volgushev, Andrei Lapets, and Azer Bestavros.
Proceedings of HotWeb 2015: The Third IEEE Workshop on Hot Topics in Web Systems and Technologies. November 2015.
Scather: Programming with Multi-party Computation and MapReduce.
Nikolaj Volgushev, Andrei Lapets, and Azer Bestavros.
Technical Report BUCS-TR-2015-010, CS Dept., Boston University, August 2015.
Web-based Multi-Party Computation with Application to Anonymous Aggregate Compensation Analytics.
Andrei Lapets, Eric Dunton, Kyle Holzinger, Frederick Jansen, and Azer Bestavros.
Technical Report BUCS-TR-2015-009, CS Dept., Boston University, August 2015.

Presentations and Talks

Privacy Preserving Route Recommendation.
Rawane Issa
DIMACS/MACS Workshop on Usable, Efficient, and Formally Verified Secure Computation.
Boston, MA, USA. March 2019.
JIFF: JavaScript Implementation of Federated Functionality.
Kinan Bab
DIMACS/MACS Workshop on Usable, Efficient, and Formally Verified Secure Computation.
Boston, MA, USA. March 2019.
Deploying MPC for Social Good. [video, slides]
Lucy Qin
Real World Crypto.
San Jose, CA, USA. January 2019.
Privacy-Preserving Data Analysis for Social Impact.
Lucy Qin
Grace Hopper Celebration.
Houston, TX, USA. September 2019.
Trustworthy Computing for Scientific Workflows. [video, slides]
Andrei Lapets and Mayank Varia.
Trusted CI Webinar Series.
July 2018.
A Survey of MPC Offerings. [video, slides]
Mayank Varia.
Differential Privacy Meets Multi-Party Computation Workshop.
Boston, MA, USA. June 2018.
JIFF: Platform for MPC Protocol Prototyping and Privacy-Preserving Web Application Development.
Andrei Lapets.
Differential Privacy Meets Multi-Party Computation Workshop.
Boston, MA, USA. June 2018.
Conclave: Secure Multi-Party Computation on Big Data.
Nikolaj Volgushev.
TPMPC 2018: Theory and Practice of Multi-Party Computation Workshop.
Aarhus, Denmark. May 2018.
De-Identification 201: Secure Multi-Party Computation.
Andrei Lapets, Mayank Varia, and Frederick Jansen.
Future of Privacy Forum.
February 2018.
Cryptographically Secure Data Analysis for Social Good. [video]
Mayank Varia.
Enigma 2018: Security and Privacy Ideas that Matter.
Santa Clara, CA, USA. January 2018.
Sharing Knowledge without Sharing Data. [video]
Azer Bestavros.
Sackler Colloquium of the National Academy of Sciences on Modeling and Visualizing Science & Technology Developments.
Irvine, CA, USA. December 2017.
Design and Deployment of Usable Web-based MPC for Data Aggregation: Lessons and Trade-offs. [video]
Andrei Lapets.
DIMACS/Northeast Big Data Hub Workshop on Privacy and Security for Big Data.
New Brunswick, NJ, USA. April 2017.
Design and Deployment of Usable, Scalable MPC.
Azer Bestavros, Andrei Lapets, Frederick Jansen, Mayank Varia (presenter), Nikolaj Volgushev, and Malte Schwarzkopf.
TPMPC 2017: Theory and Practice of Multi-Party Computation Workshop.
Bristol, UK. April 2017.
Best presentation award.

News, Reports, and Other Coverage

Armed with data, the Boston Women’s Workforce Council takes on the gender wage gap
Katie Johnston. Boston Globe.
What Is Secure Multiparty Computation?
Jessica Colarossi. BU Research.
It’s time to tell students what they need to know.
Azer Bestavros. The Washington Post.
A New Technology May Revolutionize Privacy-Preserving Data Analysis: Secure Multi-Party Computation.
Nick Hart and Kira Fatherree. Bipartisan Policy Center Blog.
BUSM receives grant to create software prototype for sharing medical data.
EurekAlert! AAAS.
Honda Begins Joint Research with Boston University in Information Security for Artificial Intelligence.
Honda World News.
Tackling the Wage Gap with Code.
Joel Brown. BU Today.
Will Data Help Close the Gender Pay Gap?
Tom Ashbrook. NPR OnPoint (radio show), WBUR 90.9.
Calculating Gender Pay Equity
Andrew Thurston. Arts & Sciences.
Computational Thinking Breaks a Logjam.
Rich Barlow. BU Today.

Current Collaborators

Faculty

Azer Bestavros
Boston University

Mayank Varia
Boston University

Ran Canetti
Boston University

Researchers and Software Engineers

Frederick Jansen Boston University	Ben Getchell Boston University	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 Boston University

External

Malte Schwarzkopf Brown University	Shannon Roberts UMass	Ada Lerner Wellesley College
Andrei Lapets Nth Party, Ltd.	Nikolaj Volgushev Alexandra Institute	Lucy Qin Brown University
Marcella Hastings University of Pennsylvania

Past Collaborators

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

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.

Accessible and ScalableSecure Multi-Party Computation