GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
Author:
Affiliation:
1. Tampere University, Tampere, Finland
2. Research Institute of Sweden (RISE), Stockholm, Sweden
Funder
EU HORIZON
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3605098.3635983
Reference31 articles.
1. Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
2. Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs
3. Alexandros Bakas, Eugene Frimpong, and Antonis Michalas. 2022. Symmetrical Disguise: Realizing Homomorphic Encryption Services from Symmetric Primitives. In International Conference on Security and Privacy in Communication Systems. Springer, 353--370.
4. Fast Homomorphic Evaluation of Deep Discretized Neural Networks
5. Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
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