Difference between revisions of "Secure Multiparty Computation"

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* [http://www.cs.bu.edu/techreports/pdf/2015-009-mpc-compensation.pdf 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.
 
* [http://www.cs.bu.edu/techreports/pdf/2015-009-mpc-compensation.pdf 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.
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You will find a demo at: https://100talent.org/
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==Allegheny County Demonstration==
 
==Allegheny County Demonstration==
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* [https://eprint.iacr.org/2018/450.pdf Galois 2018 Technical Report]
 
* [https://eprint.iacr.org/2018/450.pdf Galois 2018 Technical Report]
  
==Demo==
 
* https://100talent.org/
 
  
 
==See Also==
 
==See Also==
 
* [https://en.wikipedia.org/wiki/Secure_multi-party_computation Wikipedia article]
 
* [https://en.wikipedia.org/wiki/Secure_multi-party_computation Wikipedia article]
 
* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173185/
 
* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173185/

Revision as of 08:25, 21 January 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."

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

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.


See Also