Harmanci Arif, Chen Luyao, Kim Miran, Jiang Xiaoqian
Department of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030 USA.
Department of Mathematics, Hanyang University, Seoul 04763, Republic of Korea.
IEEE Data Descr. 2024;1:109-112. doi: 10.1109/ieeedata.2024.3482283. Epub 2024 Oct 17.
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.
为了统一测试和基准化基于Transformer的模型的安全评估，我们设计了iDASH24同态加密跟踪数据集。该数据集包括一个具有Transformer架构的蛋白质家族分类模型和一个用于构建和测试安全评估策略的示例数据集。这个数据集在iDASH24基因组隐私竞赛的挑战期被使用，各团队在该竞赛中使用同态加密方案设计了分类模型的安全评估。结合基准测试结果和配套方法，iDASH24数据集是一个独特的资源，可用于对神经网络模型的安全评估进行基准测试。
