Categories |
MACHINE LEARNING
SECURITY
PRIVACY
BIG DATA
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About |
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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. |
Call for Papers |
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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. https://easychair.org/conferences/?conf=simla2020 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 [email protected] or [email protected]) Program Committees To be announced. |
Summary |
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SiMLA 2020 : 2nd Workshop of Security in Machine Learning and its Applications will take place in Rome, Italy. It’s a 4 days event starting on Jun 22, 2020 (Monday) and will be winded up on Jun 25, 2020 (Thursday). SiMLA 2020 falls under the following areas: MACHINE LEARNING, SECURITY, PRIVACY, BIG DATA, etc. Submissions for this Workshop can be made by Mar 25, 2020. Authors can expect the result of submission by Apr 25, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before May 10, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the SiMLA 2020
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Credits and Sources |
| [1] SiMLA 2020 : 2nd Workshop of Security in Machine Learning and its Applications |
