Secure Multiparty Sampling of a Biased Coin for Differential Privacy
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54204-6_19
Reference16 articles.
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2. Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax Rates. In: IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 429–438 (2013). https://doi.org/10.1109/FOCS.2013.53
3. Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
4. Eriguchi, R., Ichikawa, A., Kunihiro, N., Nuida, K.: Efficient noise generation to achieve differential privacy with applications to secure multiparty computation. In: Borisov, N., Diaz, C. (eds.) FC 2021. LNCS, vol. 12674, pp. 271–290. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-64322-8_13
5. Clement, C., Kamath, G., Steinke, T.: The discrete gaussian for differential privacy. J. Priv. Confidentiality 12 (2022). https://doi.org/10.29012/jpc.784
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