The project studies advanced general techniques to accomplish tasks in a privacy-preserving manner. For example, these techniques enable two or more mutually distrusting entities to interact over a network to perform a joint computation on their private data, without revealing this data to each other. Many of these tools have been developed in the context of theoretical cryptography, and only recently started finding their way towards adoption. The project?s novelties are new viewpoints and techniques in the developments of these tools which take inspiration from the analysis of more conventional in-use cryptographic functionalities (like encryption). The project?s impacts are the validation of existing solutions, the development of more efficient and more secure solutions, and initiating new lines of theoretical research.

More concretely, this project introduces a new vista on zero-knowledge proofs and multi-party computation, aimed at understanding the trade-off between the concrete efficiency and the concrete security of these protocols. The goal is to analyze existing solutions, but also to propose new ones with better security and/or efficiency. While, in principle, many existing analyses can be re-examined to be made concrete, the project focuses on questions that also capture challenging technical barriers encountered in the process of giving concrete guarantees which are as precise as possible, and this, in turn, motivates new lines of theoretical research. The concrete analysis developed in this project further informs and guides the deployment of the advanced cryptographic techniques examined. As part of the broader impacts, the investigators have an outreach component aimed at training teachers and ambassadors to promote studies in STEM using cryptography.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2020-10-01
Budget End
2024-09-30
Support Year
Fiscal Year
2020
Total Cost
$1,200,000
Indirect Cost
Name
University of Washington
Department
Type
DUNS #
City
Seattle
State
WA
Country
United States
Zip Code
98195