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DSN-DSML 2019 : Second International Workshop on Dependable and Secure Machine Learning (DSML)


When Jun 24, 2019 - Jun 24, 2019
Where Portland, Oregon
Submission Deadline Mar 15, 2019
Notification Due Apr 1, 2019
Final Version Due Apr 13, 2019
Categories    machine learning   security   dependability   trust

Call For Papers

Second International Workshop on Dependable and Secure Machine Learning (DSML)
Co-located with the 49th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2019)
24 June 2019, Portland, Oregon

Call for Papers:
The DSN Workshop on Dependable and Secure Machine Learning (DSML) is an open forum for researchers, practitioners, and regulatory experts, to present and discuss innovative ideas and practical techniques and tools for producing dependable and secure machine learning (ML) systems. A major goal of the workshop is to draw the attention of the research community to the problem of establishing guarantees of reliability, security, safety, and robustness for systems that incorporate increasingly complex ML models, and to the challenge of determining whether such systems can comply with requirements for safety-critical systems. A further goal is to build a research community at the intersection of machine learning and dependable and secure computing.

Topics of Interest:
• Testing, certification, and verification of ML models and algorithms
• Metrics for benchmarking the robustness of ML systems
• Adversarial machine learning (attacks and defenses)
• Resilient and repairable ML models and algorithms
• Reliability and security of ML architectures, computing platforms, and distributed systems
• Faults in implementation of ML algorithms and their consequences
• Dependability of ML accelerators and hardware platforms
• Safety and societal impact of machine learning
• Testing, certification, and verification of ML models and algorithms

Important dates (AoE):
- Submission Deadline: 15 March, 2019
- Notification of Acceptance: 1 April, 2019
- Camera Ready: 13 April, 2019

DSML welcomes both research papers reporting results from mature work, and more speculative papers describing new ideas with preliminary exploratory work. Papers reporting industry experiences, case studies, and datasets will also be encouraged. This year we are also soliciting proposals for research talks based on work previously published elsewhere (reference to previous work is required). We strongly encourage these research talks to also include new ideas and provocative opinions and not just summarize previous work that is already published. Specifically, we accept submissions in the following formats:

- Regular research papers (up to 4 pages)
- Proposals for research talks on previously published work (1 page)

All submissions should be in PDF format and must adhere to the IEEE Computer Society 8.5″x11″ two-column camera-ready format (using a 10-point font on 12-point single-spaced leading). Both LaTeX and MS Word templates are available here:

All submitted manuscripts will be peer-reviewed by the program committee. Papers will be accepted and included in the workshop program according to the following criteria: relevance of the addressed topic to the scope of the workshop; novelty and value of the proposed contribution; scientific merit; quality of the writing, presentation accuracy, and style.
Submission site:

Authors can select either of the following options for the publication of their accepted papers:
(1) Paper will appear in the supplementary DSN proceedings (archived in the IEEE Digital library),
(2) Paper will not be included in the supplementary DSN proceedings, but the authors are required to post a version of the paper on arxiv that will be linked from the workshop website.

Organizing Committee:
- Homa Alemzadeh, University of Virginia
- Rakesh Bobba, Oregon State University
- Nicolas Papernot, Google Brain
- David Evans, University of Virginia
- Karthik Pattabiraman, University of British Columbia
- Florian Tramèr, Stanford University

Program Committee:
- Sadia Afroz, International Computer Science Institute (ICSI)
- Varun Chandrasekaran, University of Wisconsin-Madison
- Pin-Yu Chen, AI Foundations and MIT-IBM AI Lab
- Kassem M. Fawaz, University of Wisconsin - Madison
- Christian Gagne, Universite Laval
- Siva Kumar Sastry Hari, Nvidia
- Dong Seong Kim, The University of Queensland
- Bo Li, University of Illinois Urbana-Champaign
- Michael Lyu, Chinese University of Hong Kong
- Cristina Nita-Rotaru, Northeastern University
- Alina Oprea, Northeastern University
- Kush Varshney, IBM Research

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