Difference between revisions of "Secure Multiparty Computation"
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Principals: [http://cs-people.bu.edu/lapets/ Andrei Lapets], Boston University | Principals: [http://cs-people.bu.edu/lapets/ Andrei Lapets], Boston University | ||
* [https://www.boston.gov/sites/default/files/ | * [https://www.boston.gov/sites/default/files/document-file-09-2017/bwwcr-2016-new-report.pdf BOSTON WOMEN’S WORKFORCE COUNCIL REPORT 2016] . | ||
: "Working with the staff of the Hariri Institute, including Boston University Software & Application Innovation Lab interns and software engineers, we developed a completely confidential reporting system. The essence of this system is that actual wage data is never revealed outside of the company to which it relates. Rather, disguised averages are computed for each demographic category and aggregated across all companies using a technique known as secure multi-party computation, which means the BWWC receives anonymous, aggregated data. The data collected from employers was limited to a set of zip codes that defined the Greater Boston area for reporting purposes. We also asked for cash bonus information and an indication of the average seniority of the workers to provide additional background." pp. 11-12 | : "Working with the staff of the Hariri Institute, including Boston University Software & Application Innovation Lab interns and software engineers, we developed a completely confidential reporting system. The essence of this system is that actual wage data is never revealed outside of the company to which it relates. Rather, disguised averages are computed for each demographic category and aggregated across all companies using a technique known as secure multi-party computation, which means the BWWC receives anonymous, aggregated data. The data collected from employers was limited to a set of zip codes that defined the Greater Boston area for reporting purposes. We also asked for cash bonus information and an indication of the average seniority of the workers to provide additional background." pp. 11-12 | ||
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You will find a demo at: https://100talent.org/ | You will find a demo at: https://100talent.org/ | ||
==Allegheny County Demonstration== | ==Allegheny County Demonstration== | ||
Latest revision as of 08:54, 4 June 2020
Toolkits and Companies
Companies that work in this area
Software Libraries
Research
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
"Prio is a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients’ private data, except what they can infer from the aggregate statistics that the system computes."
- Prio: Private, Robust, and Scalable Computation of Aggregate Statistics, Henry Corrigan-Gibbs and Dan Boneh Stanford University, NSDI 2017
Boston University Salary Survey Work
Principals: Andrei Lapets, Boston University
- "Working with the staff of the Hariri Institute, including Boston University Software & Application Innovation Lab interns and software engineers, we developed a completely confidential reporting system. The essence of this system is that actual wage data is never revealed outside of the company to which it relates. Rather, disguised averages are computed for each demographic category and aggregated across all companies using a technique known as secure multi-party computation, which means the BWWC receives anonymous, aggregated data. The data collected from employers was limited to a set of zip codes that defined the Greater Boston area for reporting purposes. We also asked for cash bonus information and an indication of the average seniority of the workers to provide additional background." pp. 11-12
- User-Centric Distributed Solutions for Privacy-Preserving Analytics, Azer Bestavros, Andrei Lapets, Mayank Varia, Communications of the ACM, Vol. 60 No. 2, Pages 37-39, 10.1145/3029603
- Secure Multi-Party Computation for Analytics Deployed as a Lightweight Web Application, Andrei Lapets, Nikolaj Volgushev, Azer Bestavros, Frederick Jansen, Mayank Varia, Boston University CS Technical Report, August 2016.
- Web-based Multi-Party Computation with Application to Anonymous Aggregate Compensation Analytics, Andrei Lapets Eric Dunton Kyle Holzinger Frederick Jansen Azer Bestavros, Boston University CS Technical Report, September 2015.
You will find a demo at: https://100talent.org/
Allegheny County Demonstration
In 2018, Allegheny County, Galois, and the Bipartisan Policy Center demonstrated both multiparty computation and computing in secure enclaves as an approach for allowing linkage and statistical computation on multiple confidential datasets maintained by different data owners.
- 2019 BCP technical report
- 2018 BCP Press Release
- It's based on Galoi's ShareMonad Secure Multiparty Computation Platform
- Government Computing News May 31, 2019 article
- Galois 2018 Technical Report
IARPA HECTOR
IARPA has the Homomorphic Encryption Computing Techniques with Overhead Reduction (HECTOR) which is attempting to develop working prototypes using secure multiparty computation to solve real-world problems.
