Paper submission deadline: March 9, 2023 (Anywhere on Earth)
Acceptance Notification: April 19, 2023
Camera-ready deadline: May 1, 2023 (Anywhere on Earth)
SiMLA Conference: 19 June, 2023 (15:30 - 18:30 JST (UTC+9))
As the development of computing hardware, algorithms, and more importantly, the availability of a 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 a deep understanding of adversarial machine learning, one of the unique vulnerabilities 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 the general public is becoming more concerned about their 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 algorithms 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 are not limited, to the following:
Authors are welcome to submit their papers in the following two forms:
Full papers that present relatively mature research results related to security issues of machine learning algorithms, systems, and applications. The paper could be an attack, defense, security analysis, surveys, etc. The submissions for this type must follow the original LNCS format (see LNCS format) with a page limit of 18 pages (including references) for the main part (reviewers are not required to read beyond this limit) and 20 pages in total. Note that the page limit for the camera-ready paper is set to a maximum of 20 pages (in LNCS format).
Short papers that describe ongoing work and bring some new insights and inspiring ideas related to security issues of machine learning algorithms, systems, and applications. Short papers will follow the same LNCS format as full paper (see LNCS format), but with a page limit of 9 pages (including references).
The submissions must be anonymous, with no author names, affiliations, acknowledgment, or obvious references. Once accepted, the papers will appear in the formal proceedings. Authors of accepted papers must guarantee that their papers will be presented at the conference and must make their papers available online. There will be the best paper award.
EasyChair System will be used for paper submission.
Submission deadline has passed.
Please submit your paper via Easychair: Easychair submission link.
Each workshop affiliated with ACNS 2023 will nominate the best paper candidates. Best workshop papers will be selected and awarded a 500 EUR prize sponsored by Springer.
There will be 1-2 invited speakers in the workshop.
|Ezekiel Soremekun||Royal Holloway, University of London||Workshop Chair|
|Badr Souani||SnT, University of Luxembourg||Web Chair|
|Salah Ghamizi||SnT, University of Luxembourg||Publicity Chair|
|Alexander Bartel||Umeå University|
|Apratim Bhattacharyya||Qualcomm AI Research|
|Ezekiel Soremekun||Royal Holloway, University of London|
|Martin Gubri||SnT, University of Luxembourg|
|Maxime Cordy||SnT, University of Luxembourg|
|Sakshi Udeshi||Lumeros AI|
|Salah Ghamizi||SnT, University of Luxembourg|
|Sudipta Chattopadhyay||Singapore University of Technology and Design|
|Wang Jingyi||Zhejiang University|
Please Register Here.
Registration is free for students.
|Time Table : 19th June, 2023 (Virtual)|
|15:45||6:45||Invited Talk||Salah Ghamizi||Speaker Name: Prof. Masashi Sugiyama
Affiliation: RIKEN Center for Advanced Intelligence Project
Title: "Towards Trustworthy Machine Learning from Weakly Supervised, Noisy, and Biased Data"
|17:00||8:00||Paper (30 min each)||Ezekiel Soremekun||(1) Aldin Vehabovic, Hadi Zanddizari, Farooq Shaikh, Nasir Ghani, Morteza Safaei Pour, Elias Bou Harb and Jorge Crichigno. Federated Learning Approach for Distributed Ransomware Analysis
(2) Mohammed M. Alani, Atefeh Mashatan and Ali Miri. Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks
(3) Haibo Zhang, Zhihua Yao and Kouichi Sakurai. Eliminating Adversarial Perturbations Using Image-to-Image Translation Method
For more information, please contact the organizer Ezekiel Soremekun