Stolen Risks of Models with Security Properties-Reference-Cited by-同舟云学术

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Stolen Risks of Models with Security Properties

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
language:
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Author:

Qin Yue¹^ORCID,Fu Zhuoqun²^ORCID,Deng Chuyun²^ORCID,Liao Xiaojing¹^ORCID,Zhang Jia²^ORCID,Duan Haixin³^ORCID

Affiliation:

1. Indiana University Bloomington, Bloomington, IN, USA

2. Tsinghua University, Beijing, China

3. Tsinghua University & Zhongguancun Laboratory, Beijing, China

Funder

NSF CNS

Indiana University Institute for Advanced Study (IAS)

Grant Thornton Institute

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3576915.3616653

Reference58 articles.

1. 2019. Medical Insurance Dataset. https://www.kaggle.com/datasets/ rajgupta2019/medical-insurance-dataset.

2. 2022. Customer Churn Dataset. https://www.kaggle.com/code/yejiseoung/ building-gradient-boosting-pipeline-0-99-roc-auc/data.

3. 2023. Code dataset and full version paper. https://sites.google.com/view/ maskedthief/home.

4. David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. Advances in neural information processing systems, Vol. 32 (2019).

5. Evasion Attacks against Machine Learning at Test Time

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