과제정보
이 논문은 2024년도 BK21 FOUR 정보기술 미래인재 교육연구단에 의하여 지원되었음. 이 논문은 2024년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (RS-2023-00277326). 이 논문은 2024년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (IITP-2023-RS-2023-00256081)
참고문헌
- Gilad-Bachrach, Ran, et al., "Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy.", International conference on machine learning. PMLR, 2016.
- Chou, Edward, et al., "Faster cryptonets: Leveraging sparsity for real-world encrypted inference." arXiv preprint arXiv:1811.09953 (2018).
- Lee, Eunsang, et al., "Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions." International Conference on Machine Learning. PMLR, 2022.
- Jha, Nandan Kumar, et al., "Deepreduce: Relu reduction for fast private inference." International Conference on Machine Learning. PMLR, 2021.
- Lou, Qian, et al., "Safenet: A secure, accurate and fast neural network inference." International Conference on Learning Representations. 2020.
- Ao, Wei, and Vishnu Naresh Boddeti., "Autofhe: Automated adaption of cnns for efficient evaluation over fhe." arXiv preprint arXiv:2310.08012 (2023).
- Park, Jaiyoung, et al., "AESPA: Accuracy preserving low-degree polynomial activation for fast private inference." arXiv preprint arXiv:2201.06699 (2022).
- Jang, Jaehee, et al. "Privacy-preserving deep sequential model with matrix homomorphic encryption." Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security. 2022.
- Lee, Junghyun, et al., "Optimizing layerwise polynomial approximation for efficient private inference on fully homomorphic encryption: a dynamic programming approach." arXiv preprint arXiv:2310.10349 (2023).