The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference
Abstract
Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted data. Ló pez-Alt et al. (STOC 2012) proposed a generalized notion of HE, called Multi-Key Homomorphic Encryption (MKHE), which is capable of performing arithmetic operations on ciphertexts encrypted under different keys. In this paper, we present multi-key variants of two HE schemes with packed ciphertexts. We present new relinearization algorithms which are simpler and faster than previous method by Chen et al. (TCC 2017). We then generalize the bootstrapping techniques for HE to obtain multi-key fully homomorphic encryption schemes. We provide a proof-of-concept implementation of both MKHE schemes using Microsoft SEAL. For example, when the dimension of base ring is 8192, homomorphic multiplication between multi-key BFV (resp. CKKS) ciphertexts associated with four parties followed by a relinearization takes about 116 (resp. 67) milliseconds. Our MKHE schemes have a wide range of applications in secure computation between multiple data providers. As a benchmark, we homomorphically classify an image using a pre-trained neural network model, where input data and model are encrypted under different keys. Our implementation takes about 1.8 seconds to evaluate one convolutional layer followed by two fully connected layers on an encrypted image from the MNIST dataset.
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Cites
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Self-citationSecureML: A System for Scalable Privacy-Preserving Machine Learning
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(Leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090262 · Cited by 1,782
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Oblivious Neural Network Predictions via MiniONN Transformations
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Zhang, L., Saito, H., Yang, L., & Wu, J. (2022) · IEEE Access · doi:10.1109/access.2022.3192454 · Cited by 54
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Zhang, Q., Xin, C., & Wu, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058638 · Cited by 53
HEAX: An Architecture for Computing on Encrypted Data
Riazi, M. S., Laine, K., Pelton, B., & Dai, W. (2019) · arXiv · doi:10.48550/arxiv.1909.09731 · Cited by 26
Self-citationA Secure Cloud-Edge Collaborative Logistic Regression Model
Wang, C., Xu, J., & Yin, L. (2021) · 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00049 · Cited by 8
Metadata
- Authors
- Chen, H., Dai, W., Kim, M., & Song, Y.
- Venue
- Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2019-11-06
- Volume
- 2019
- Pages
- 395-412
- OpenAlex
- W2954783273
- Cited by
- 256
- References
- 26
- FWCI
- 8.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, H., Dai, W., Kim, M., & Song, Y. (2019). Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. https://doi.org/10.1145/3319535.3363207
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