SPAI 2020: 1st Workshop of Security and Privacy on Artificial Intelligence Taipei, Taiwan, June 1, 2020 |
Conference website | https://sites.google.com/view/spai2020/home |
Submission link | https://easychair.org/conferences/?conf=spai2020 |
Submission deadline | March 15, 2020 |
SPAI 2020 is a single track workshop of cutting edge and state-of-art research talks covering topics of two major categories, AI/ML Security and related applications for computer security and privacy. The workship will be held in Taipei, Taiwan, in conjunction with ASIA CCS 2020 (https://asiaccs2020.cs.nthu.edu.tw/). SPAI 2020 solicts security and privacy research on AI/ML, such as new approach in developing adversarial samples to evade the current machine learning mechanism, or defense like privacy-preserving machine learninig. Not only about AI Security, we also solicit novel ideas and works in applying AI/ML as solution to classic security and privacy problems, such as bot and spam detection, data leak and loss detection, or malware or binary analysis.
Submission Guidelines
Submission must be written in English, and layout in double-column ACM SIG Proceedings format, and should not exceed 8 page excluding biography and appendices (at most 10 pages in total). All submissions should be appropriately anonymized. Submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Only PDF files will be accepted and authors of accepted papers must guarantee that their papers will be presented at the workshop. Position papers describing the work in progress are also welcome. At least one author of the paper must be registered at both the main AsiaCCS conference and this workshop. Accepted papers will be published in the ACM Digital Library.
Paper submission link: https://easychair.org/conferences/?conf=spai2020
List of Topics
AI/ML Security
- Adversarial attacks on machine learning and artificial intelligence
- Defenses against adversarial attacks
- Foundation of Artificial Intelligence Security
- Privacy issues and privacy-preserving machine learning
- Explainability and Fairness
AL/ML Applications for Computer Security and Privacy
- Malware detection and analysis
- Intrusion detection and classification
- Data leak detection and Data loss prevention
- Data anonymization and de-anonymization
- Spam, phishing, and bot detection
- Vulnerability discovery and automation
- Threat intelligent management and monitoring
- Novel industrial use cases for adapting AL/ML for security and privacy
Committees
Program Chairs
- Yueh-Hsun Lin, Apple
- Xinyu Xing, Pennsylvania State University
Program committee
- Mohammad Al-Rubaie, Facebook
- Pin-Yu Chen, IBM Research
- Yueqiang Cheng, Baidu Research
- Neil Gong, Duke University
- Wenbo Guo, Pennsylvania State University
- Peng Li, Baidu Research
- Tongbo Luo, JD.COM R&D Center
- Fengguo Wei, Google
- Johnny Wang, Google
- Lun Wang, UC Berkeley
- Ting Wang, Pennsylvania State University
- Jankins Zhan, Netskope
Contact
All questions about submissions should be emailed to spai2020 at easychair.org