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SiMLA 2021 - Security in Machine Learning and its Applications (SiMLA 2021)

Date2021-06-21 - 2021-06-24


VenueKamakura, Japan Japan



Topics/Call fo Papers

As the development of computing hardware, algorithms, and more importantly, availability of large volume of data grows, machine learning technologies have become increasingly popular. Practical systems have been deployed in various domains, like face recognition, automatic video monitoring, and even auxiliary driving. However, the security implications of machine learning algorithms and systems are still unclear. For example, developers still lack deep understanding on adversarial machine learning, one of the unique vulnerability of machine learning systems, and are unable to evaluate the robustness of those machine learning algorithms effectively. The other prominent problem is privacy concerns when applying machine learning algorithms, and as general public are becoming more concerned about their own privacy, more works are definitely desired towards privacy preserving machine learning systems.
Motivated by this situation, this workshop solicits original contributions on the security and privacy problems of machine learning algorithm and systems, including adversarial learning, algorithm robustness analysis, privacy preserving machine learning, etc. We hope this workshop can bring researchers together to exchange ideas on cutting-edge technologies and brainstorm solutions for urgent problems derived from practical applications.
Topics of interest include, but not limited, to followings:
Adversarial Machine Learning
Robustness Analysis of Machine Learning Algoritms
Detection and Defense to Training Data set Poison attack (including backdoor attacks)
Privacy Preserving Machine Learning
Watermarking of Machine Learning Algorithms and Systems
Attack and defense of face recognition systems
Attacks and defense of voice recognition and voice commanded systems
Attacks and defense of machine learning algorithms in program analysis
Malware identification and analysis
Spam and phishing email detection
Vulnerability analysis

Last modified: 2020-12-27 10:32:53