Securing Approximate Homomorphic Encryption Using Differential Privacy

Author:

Li BaiyuORCID,Micciancio DanieleORCID,Schultz MarkORCID,Sorrell JessicaORCID

Publisher

Springer Nature Switzerland

Reference33 articles.

1. Albrecht, M., et al.: Homomorphic encryption security standard. Technical report, HomomorphicEncryption.org, Toronto, Canada, November 2018. https://homomorphicencryption.org/standard/

2. Bai, S., Lepoint, T., Roux-Langlois, A., Sakzad, A., Stehlé, D., Steinfeld, R.: Improved security proofs in lattice-based cryptography: using the Rényi divergence rather than the statistical distance. J. Cryptol. 31(2), 610–640 (2018)

3. Canonne, C.L.: A survey on distribution testing: your data is big. But is it blue? Theory of Computing, pp. 1–100 (2020)

4. Canonne, C.L., Kamath, G., Steinke, T.: The discrete gaussian for differential privacy. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 15676–15688. Curran Associates Inc. (2020)

5. Cheon, J.H., et al.: Toward a secure drone system: flying with real-time homomorphic authenticated encryption. IEEE Access 6, 24325–24339 (2018)

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