GMS: an efficient fully homomorphic encryption scheme for secure outsourced matrix multiplication
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11227-024-06449-3.pdf
Reference29 articles.
1. Zhang P, Huang T, Sun X et al (2023) Privacy-preserving and outsourced multi-party k-means clustering based on multi-key fully homomorphic encryption. IEEE Trans Dependable Secure Comput 20(3):2348–2359. https://doi.org/10.1109/tdsc.2022.3181667
2. Zhao L, Chen L (2018) Sparse matrix masking-based non-interactive verifiable (outsourced) computation, revisited. IEEE Trans Dependable Secure Comput 17(6):1188–1206. https://doi.org/10.1109/tdsc.2018.2861699
3. Duong DH, Mishra PK, Yasuda M (2016) Efficient secure matrix multiplication over lwe-based homomorphic encryption. Tatra Mt Math Publ 67(1):69–83. https://doi.org/10.1515/tmmp-2016-0031
4. Huang H, Zong H (2023) Secure matrix multiplication based on fully homomorphic encryption. J Supercomput 79(5):5064–5085. https://doi.org/10.1007/s11227-022-04850-4
5. Zhu L, Hua Q, Chen Y, et al (2023) Secure outsourced matrix multiplication with fully homomorphic encryption. In: European Symposium on Research in Computer Security, Springer, pp 249–269, https://doi.org/10.1007/978-3-031-50594-2_13
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