To provide a standardized approach for testing and benchmarking secure evaluation of transformer-based models, we developed the iDASH24 Homomorphic Encryption track dataset. This dataset is centered on protein sequence classification as the benchmark task. It includes a neural network model with a transformer architecture and a sample dataset, both used to build and evaluate secure evaluation strategies.

Dataset Files

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[1] Arif Harmanci, "iDASH24 Secure Evaluation of Neural Networks for Protein Classification ", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/9fdg-pz55. Accessed: Feb. 17, 2025.
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url = {http://dx.doi.org/10.21227/9fdg-pz55},
author = {Arif Harmanci },
publisher = {IEEE Dataport},
title = {iDASH24 Secure Evaluation of Neural Networks for Protein Classification },
year = {2024} }
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T1 - iDASH24 Secure Evaluation of Neural Networks for Protein Classification
AU - Arif Harmanci
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Arif Harmanci. (2024). iDASH24 Secure Evaluation of Neural Networks for Protein Classification . IEEE Dataport. http://dx.doi.org/10.21227/9fdg-pz55
Arif Harmanci, 2024. iDASH24 Secure Evaluation of Neural Networks for Protein Classification . Available at: http://dx.doi.org/10.21227/9fdg-pz55.
Arif Harmanci. (2024). "iDASH24 Secure Evaluation of Neural Networks for Protein Classification ." Web.
1. Arif Harmanci. iDASH24 Secure Evaluation of Neural Networks for Protein Classification [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/9fdg-pz55
Arif Harmanci. "iDASH24 Secure Evaluation of Neural Networks for Protein Classification ." doi: 10.21227/9fdg-pz55