Literaturverzeichnis zum Artikel „Maschinelles Lernen und Datenschutz“
Beitrag in der COMPUTER SPEZIAL 2/2023[1] https://de.wikipedia.org/wiki/Maschinelles_Lernen aufgerufen am 30.08.2023
[2] https://www.gesetze-im-internet.de/bdsg_2018/ aufgerufen am 08.09.2023
[3] „When Machine Learning Meets Privacy: A Survey and Outlook“ Liu et al., ACM Computing Surveys, Vol. 54, No. 2 Art. 31, März 2021 https://doi.org/10.1145/3436755
[4] "The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks" Zhang et al. CVPR 2020 https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_The_Secret_Revealer_Generative_Model-Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2020_paper.html
[5] "Re-Thinking Model Inversion Attacks Against Deep Neural Networks" Nguyen et al. CVPR 2023 - https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html
[6] „On the Protection of Private Information in Machine Learning Systems: Two Recent Approches“ Abadi et al., CSF 2017 https://doi.org/10.1109/CSF.2017.10
[7] „Communication-Efficient Learning of Deep Networks from Decentralized Data“ McMahan et al., AISTATS 2017, Vers. Jan. 2023 https://arxiv.org/abs/1602.05629v4
[8] „On the Role of Data Anonymization in Machine Learning Privacy“ Senavirathne und Torra, TrustCom 2020 https://doi.org/10.1109/TrustCom50675.2020.00093
[9] „Generating Artificial Data for Private Deep Learning“ Triastcyn und Faltings, PAL 2019 https://ceur-ws.org/Vol-2335/1st_PAL_paper_7.pdf
[10] https://support.google.com/messages/answer/9327902 aufgerufen am 30.08.2023
[11] „Split learning for health: Distributed deep learning without sharing raw patient data“, Vepakomma et al., ICLR AISG 2019 (2018) https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf
[12] „NoPeek: Information leakage reduction to share activations in distributed deep learning“ Vepakomma et al., ICDMW 2020 https://doi.org/10.1109/ICDMW51313.2020.00134
[13] „No free lunch theorems for optimization“, Wolpert und Macready, IEEE Transactions on Evolutionary Computation, Vol. 1, Iss. 1, April 1997, https://doi.org/10.1109/4235.585893
