BDPL: A boundary differentially private layer against machine learning model extraction attacks

H Zheng, Q Ye, H Hu, C Fang, J Shi - … 23–27, 2019, Proceedings, Part I 24, 2019 - Springer
Abstract Machine learning models trained by large volume of proprietary data and intensive
computational resources are valuable assets of their owners, who merchandise these …
被引用次数：62 相关文章

BDPL: a boundary differentially private layer against machine learning model extraction attacks

H Zheng, Q Ye, H Hu, C Fang, J Shi - 2019 - ira.lib.polyu.edu.hk
Issue Date: 2019 Source: Lecture notes in computer science (including subseries Lecture
notes in artificial intelligence and lecture notes in bioinformatics), 2019, v. 11735, p. 66-83 …

BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks

H Zheng, Q Ye, H Hu, C Fang… - … European Symposium on …, 2019 - research.polyu.edu.hk
Abstract Machine learning models trained by large volume of proprietary data and intensive
computational resources are valuable assets of their owners, who merchandise these …
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[PDF][PDF] BDPL: A boundary differentially private layer against machine learning model extraction attacks

H Zheng, Q Ye, H Hu, C Fang, J Shi - … Security–ESORICS 2019 …, 2019 - drive.google.com
Machine learning models trained by large volume of proprietary data and intensive
computational resources are valuable assets of their owners, who merchandise these …

BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks

H Zheng, Q Ye, H Hu, C Fang, J Shi - European Symposium on …, 2019 - dl.acm.org
Abstract Machine learning models trained by large volume of proprietary data and intensive
computational resources are valuable assets of their owners, who merchandise these …