Balanced Privacy Budget Allocation for Privacy-Preserving Machine Learning

Author:

He BingchangORCID,Miyaji AtsukoORCID

Publisher

Springer Nature Switzerland

Reference11 articles.

1. Duchi, J.C., Jordan, E.: Local privacy and statistical minimax rates. In: 54th Annual Symposium on Foundations of Computer Science, pp. 429–438. IEEE (2013)

2. Feng, J., Yang, L.T., Nie, X., Gati, N.J.: Edge-cloud-aided differentially private tucker decomposition for cyber-physical-social systems. IEEE Internet Things J. 9(11), 8387–8396 (2020)

3. Hu, H., Salcic, Z., Sun, L., Dobbie, E.: Membership inference attacks on machine learning: a survey. ACM Comput. Surv. (CSUR) 54, 1–37 (2021)

4. Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014)

5. Miyaji, A., Takahashi, T., Wang, P., Yamatsuki, T., Mimoto, T.: Privacy-preserving data analysis without trusted third party. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022, pp. 710–717. IEEE (2022)

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