Bernau, D., Eibl, G., Grassal, P., Keller, H., & Kerschbaum, F. (2021). Quantifying identifiability to choose and audit epsilon in differentially private deep learning. Proceedings of the VLDB Endowment, 14(13), . https://doi.org/10.14778/3484224.3484231
Chicago-Zitierstil (17. Ausg.)Bernau, Daniel, Günther Eibl, Philip-William Grassal, Hannah Keller, und Florian Kerschbaum. "Quantifying Identifiability to Choose and Audit Epsilon in Differentially Private Deep Learning." Proceedings of the VLDB Endowment 14, no. 13 (2021). https://doi.org/10.14778/3484224.3484231.
MLA-Zitierstil (9. Ausg.)Bernau, Daniel, et al. "Quantifying Identifiability to Choose and Audit Epsilon in Differentially Private Deep Learning." Proceedings of the VLDB Endowment, vol. 14, no. 13, 2021, https://doi.org/10.14778/3484224.3484231.