Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57543-3_8
Reference48 articles.
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2. Albrecht, M.R., et al.: Homomorphic encryption standard. IACR Cryptol. ePrint Arch., p. 939 (2019)
3. Albrecht, M.R., Player, R., Scott, S.: On the concrete hardness of learning with errors. J. Math. Cryptol. 9(3), 169–203 (2015)
4. Badawi, A.A., Hoang, L., Mun, C.F., Laine, K., Aung, K.M.M.: Privft: private and fast text classification with homomorphic encryption. CoRR arxiv:1908.06972 (2019)
5. Barker, E.: Recommendation for key management, part 1: General (2016). https://doi.org/10.6028/NIST.SP.800-57pt1r4
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