MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably

Author:

Alon BarORCID,Naor MoniORCID,Omri EranORCID,Stemmer UriORCID

Publisher

Springer Nature Switzerland

Reference42 articles.

1. Alon, B., Naor, M., Omri, E., Stemmer, U.: MPC for tech giants (GMPC): enabling Gulliver and the lilliputians to cooperate amicably. Cryptology ePrint Archive, Paper 2022/902 (2022). https://eprint.iacr.org/2022/902

2. Avent, B., Dubey, Y., Korolova, A.: The power of the hybrid model for mean estimation. Proc. Priv. Enhancing Technol. 2020(4), 48–68 (2020)

3. Avent, B., Korolova, A., Zeber, D., Hovden, T., Livshits, B.: BLENDER: enabling local search with a hybrid differential privacy model. In: 26th USENIX Security Symposium (USENIX Security 17), pp. 747–764. USENIX Association (2017)

4. Balle, B., Bell, J., Gascón, A., Nissim, K.: The privacy blanket of the shuffle model. In: Boldyreva, A., Micciancio, D. (eds.) Advances in Cryptology – CRYPTO 2019: 39th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 18–22, 2019, Proceedings, Part II, pp. 638–667. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-26951-7_22

5. Balle, B., Bell, J., Gascón, A., Nissim, K.: Private summation in the multi-message shuffle model. In: CCS, pp. 657–676. ACM (2020)

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