Memory Efficient Privacy-Preserving Machine Learning Based on Homomorphic Encryption
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54773-7_13
Reference46 articles.
1. Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
2. Aharoni, E., et al.: HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data. Proceedings on Privacy Enhancing Technologies 2023(1), 325–342 (Jan 2023). https://doi.org/10.56553/popets-2023-0020, http://arxiv.org/abs/2011.01805, arXiv:2011.01805 [cs]
3. Akavia, A., Oren, N., Sapir, B., Vald, M.: Compact storage for homomorphic encryption. Cryptology ePrint Archive (2022)
4. Al Badawi, A., et al.: Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs. IEEE Trans. Emerg. Topics Comput. (2020). https://doi.org/10.1109/TETC.2020.3014636, conference Name: IEEE Transactions on Emerging Topics in Computing
5. Al Badawi, A., et al.: OpenFHE: open-source fully homomorphic encryption library. in: proceedings of the 10th workshop on encrypted computing & applied homomorphic cryptography, pp. 53–63. WAHC’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3560827.3563379 event-place: Los Angeles, CA, USA
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