Today, vast amounts of private datasets are stored by individuals and institutions on personal electronic devices and cloud servers. Many applications of tremendous societal impact can benefit from the ability to perform joint computations on these datasets. However, in the presence of malicious actors -- whether it be the entities performing the computation or external processes -- this benefit is associated with a significant risk of losing data privacy. The powerful paradigm of secure computation in cryptography aims to resolve the conflict between computation and privacy by providing a distributed mechanism for computing upon the private data of individuals, so that nothing beyond the output of the computation is revealed.

Feasibility results for secure computation were established three decades ago, with an immense promise for the future. Over the years, however, secure computation has found limited real-world applicability. The primary reason for this disparity is that the protocol complexity of known solutions does not meet the demands of applications. To bring secure computation closer to real world applications, this project focuses on a fundamental metric of efficiency -- the number of required rounds of interaction between the parties -- and aims to fully resolve the round complexity of secure computation, leading to optimal protocols. To achieve this goal, the project develops new, low-overhead methods to achieve security against malicious adversaries, who may arbitrarily deviate from their prescribed strategies. To promote confident use in real world, the project develops solutions in the setting of minimal trust, where an individual does not need to trust any entity or external process beyond itself.

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.

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Johns Hopkins University
United States
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