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