Faculty, Staff and Student Publications

Publication Date

1-1-2024

Abstract

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

DOI

10.1109/ieeedata.2024.3482283

PMID

39712862

PMCID

PMC11660429

PubMedCentral® Posted Date

12-20-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

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