Security in Machine Learning and its Applications (SiMLA 2020)

June 22-25, 2020October 19-22, 2020, Rome, Italy (Co-located with ACNS 2020)

Important Dates

Workshop Background

As the development of computing hardware, algorithms, and more importantly, availability of large volume of data, machine learning technologies have become a 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, people 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.

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 edge-cutting technologies and brainstorm solutions for urgent problems derived from practical applications.


Topics of interest include, but not limited, to followings:

Submissions Guidelines

Authors are welcome to submit their papers in following two forms:

The submissions must be anonymous, with no author names, affiliations, acknowledgement or obvious references. Once accepted, the papers will appear in the formal proceedings. Authors of accepted papers must guarantee that their paper will be presented at the conference and must make their paper available online. There will be a best paper award.

Authors should consult Springer's authors' guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form, through which the copyright for their paper is transferred to Springer. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.

EasyChair System will be used for paper submission.

Please submit your paper via the following link EasyChair System.

Workshop Chairs

Zhou Li (University of California, Irvine)

Kehuan Zhang (The Chinese University of Hong Kong)

(If there is any question, please contact the workshop chair at or

Program Committees

Zhou Li, University of California, Irvine

Kangkook Jee, The University of Texas at Dallas

Baojun Liu, Tsinghua University

Wenrui Diao, Shandong University

Kehuan Zhang, The Chinese University of Hong Kong

Yinqian Zhang, The Ohio State University

Di Tang, The Chinese University of Hong Kong

Zhe Zhou, Fudan University

Kai Chen, Institute of Information Engineering, Chinese Academy of Sciences

Chaowei Xiao, university of Michigan, Ann Arbor