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Privacy Risks of Securing Machine Learning Models against Adversarial Examples. In2019 CCS. 241--257.  Liwei Song Reza Shokri and Prateek Mittal. 2019. Privacy Risks of Securing Machine Learning Models against Adversarial Examples. In2019 CCS. 241--257.","DOI":"10.1145\/3319535.3354211"},{"key":"e_1_3_2_2_42_1","unstructured":"Florian Tram\u00e8r Fan Zhang Ari Juels Michael K. Reiter and Thomas Ristenpart. 2016. Stealing Machine Learning Models via Prediction APIs. In USENIX. 601--618.  Florian Tram\u00e8r Fan Zhang Ari Juels Michael K. Reiter and Thomas Ristenpart. 2016. Stealing Machine Learning Models via Prediction APIs. In USENIX. 601--618."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Binghui Wang and Neil Zhenqiang Gong. 2018. Stealing Hyper parameters in Machine Learning. In 2018 S&P. 36--52.  Binghui Wang and Neil Zhenqiang Gong. 2018. Stealing Hyper parameters in Machine Learning. 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