%0 Journal Article %1 journals/pvldb/BernauEGKK21 %A Bernau, Daniel %A Eibl, Günther %A Grassal, Philip-William %A Keller, Hannah %A Kerschbaum, Florian %D 2021 %J Proc. VLDB Endow. %K dblp %N 13 %P 3335-3347 %T Quantifying identifiability to choose and audit epsilon in differentially private deep learning. %U http://dblp.uni-trier.de/db/journals/pvldb/pvldb14.html#BernauEGKK21 %V 14

@article{journals/pvldb/BernauEGKK21, added-at = {2022-04-21T00:00:00.000+0200}, author = {Bernau, Daniel and Eibl, Günther and Grassal, Philip-William and Keller, Hannah and Kerschbaum, Florian}, biburl = {https://www.bibsonomy.org/bibtex/22ed8a1553d2e7f2c3379c4dc5523151c/dblp}, ee = {https://doi.org/10.14778/3484224.3484231}, interhash = {8d9860bf7e45d77f0cd0c337bf81251a}, intrahash = {2ed8a1553d2e7f2c3379c4dc5523151c}, journal = {Proc. VLDB Endow.}, keywords = {dblp}, number = 13, pages = {3335-3347}, timestamp = {2024-04-09T01:03:58.000+0200}, title = {Quantifying identifiability to choose and audit epsilon in differentially private deep learning.}, url = {http://dblp.uni-trier.de/db/journals/pvldb/pvldb14.html#BernauEGKK21}, volume = 14, year = 2021 }