TY - GEN
T1 - SoK
T2 - 3rd IEEE European Symposium on Security and Privacy, EURO S and P 2018
AU - Papernot, Nicolas
AU - McDaniel, Patrick
AU - Sinha, Arunesh
AU - Wellman, Michael P.
N1 - Funding Information:
We thank Martín Abadi, Z. Berkay Celik, Ian Good-fellow, Damien Octeau, and Kunal Talwar for feedback on early versions of this document. We also thank Megan McDaniel for taking good care of our diet before the deadline. We would also like to thank Seda Guerses for shepherding our paper. Nicolas Papernot is supported by a Google PhD Fellowship in Security. Research was supported in part by the Army Research Laboratory, under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA), and the Army Research Office under grant W911NF-13-1-0421. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/6
Y1 - 2018/7/6
N2 - Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive - new systems and models are being deployed in every domain imaginable, leading to widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date.We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. In particular, it is apparent that constructing a theoretical understanding of the sensitivity of modern ML algorithms to the data they analyze, à la PAC theory, will foster a science of security and privacy in ML.
AB - Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive - new systems and models are being deployed in every domain imaginable, leading to widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date.We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. In particular, it is apparent that constructing a theoretical understanding of the sensitivity of modern ML algorithms to the data they analyze, à la PAC theory, will foster a science of security and privacy in ML.
UR - http://www.scopus.com/inward/record.url?scp=85050721582&partnerID=8YFLogxK
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U2 - 10.1109/EuroSP.2018.00035
DO - 10.1109/EuroSP.2018.00035
M3 - Conference contribution
AN - SCOPUS:85050721582
T3 - Proceedings - 3rd IEEE European Symposium on Security and Privacy, EURO S and P 2018
SP - 399
EP - 414
BT - Proceedings - 3rd IEEE European Symposium on Security and Privacy, EURO S and P 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 April 2018 through 26 April 2018
ER -