
IDS Distinguished Speaker Series #7: Security Of AI, By AI and For AI: Charting New Territories in AI-Centered Cybersecurity Research
Venue: Tam Wing Fan Inno Wing II / Zoom
Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.
Abstract
The rapid advancements in artificial intelligence (AI) technologies and the unyielding demand for their transformative applications have ushered in significant opportunities for security and privacy research and innovations. There is an urgent need for innovative and practical solutions to protect data and other assets to support the training and utilization of large, complicated machine learning (ML) models in a scalable and cost-effective manner (“Security For AI”). In the meantime, substantial research efforts are focused on understanding the security and privacy implications of AI systems, particularly identification of vulnerabilities in ML models and mitigation of associated risks (“Security Of AI”). Furthermore, cutting-edge AI technologies are increasingly being deployed to enhance the security of computing systems, offering intelligent protection and more effective defenses against real-world threats (“Security By AI”).
In this presentation, I will use our research in these areas to demonstrate how AI innovations have expanded the horizons of security and privacy research. For instance, under the theme “Security For AI,” I will provide an overview of ongoing research at the Center for Distributed Confidential Computing (CDCC) — one of the largest initiatives funded by the US National Science Foundation aimed at advancing practical, scalable data-in-use protection. This initiative is poised to have a transformative impact on AI research. Regarding “Security Of AI,” I will discuss our investigations into Trojan threats to ML models, exploring the fundamentality of this emerging security risk, its defensibility in particular. In the context of “Security By AI,” I will showcase how AI and ML technologies are revolutionizing the detection and prediction of security threats within carrier networks—a vital infrastructure—by automating the analysis of their documentations. Lastly, I will discuss potential future directions in the vast space of AI-centered cybersecurity research and innovations.
Speaker
Prof. XiaoFeng Wang is the Associate Dean for Research and a James H. Rudy Professor of Luddy School of Informatics, Computing and Engineering, Indiana University at Bloomington and a Fellow of ACM, IEEE and AAAS. At IU, he is also a Co-Director of Center for Security and Privacy in Informatics, Computing and Engineering, and was the Director of the Master of Science in Secure Computing (MSSC) program.
Prof. Wang serves as Director and Lead PI of Center for Distributed Confidential Computing (CDCC), a Frontiers Project in Secure and Trustworthy Computing funded by the National Science Foundation. The project is a multi-institution effort, involving faculty from IU (Lead), CMU, Duke, OSU, Penn State, Purdue, Spelman and Yale. The center aims at laying the technological foundations for practical data-in-use protection based on Trusted Execution Environments (TEE) over today and tomorrow’s cloud and edge platforms, which is critical to the advance of AI and data science.
Prof. Wang is the Chair of ACM Special Interest Group on Security, Audit and Control (SIGSAC), and was also TPC Co-Chair of the ACM Conference on Computer and Communications Security (CCS), the ACM’s flagship security and privacy conference, during 2018 and 2019. In the past 20 years, Prof. Wang has been working on a broad range of research topics in systems security and data privacy. He is considered to be one of the most prominent systems security and privacy researchers, a top author according to online statistics such as CSRankings, System Security Circus (Eurecom), and Top Authors, the Systems Cirus (EPFL). Prof. Wang is known for his high-impact research on security analysis of real-world systems and biomedical data privacy. Particularly, the projects he led on side-channel analysis and mitigation, payment and single-sign-on API integrations, Android and iOS security and IoT protection have changed the way the industry built computing systems. Also he is a pioneer researcher on human genome privacy and a co-founder of the iDASH Genome Privacy Competition that contributes to reducing the gap between security and cryptography research and real-world demands for biomedical data sharing and computing protection. More recently, he is actively working on TEE-based Data-in-Use protection for supporting AI, Trustworthy AI, and application of AI technologies (such as NLP and deep learning) to protect computing systems, LTE/5G networks in particular.
For his work, Prof. Wang has received numerous awards, including Award for Outstanding Research in Privacy Enhancing Technologies (the PET Award), Best Practical Paper Award at the 32nd IEEE Symposium on Security and Privacy (IEEE S&P Oakland), and two Distinguished Paper Awards at the 26th Network and Distributed System Security Symposium (NDSS). His work has been extensively reported by public media, including CNN, New York Times, Wall Street Journal, MSNBC, Forbes, Slashdot, Nature News, etc.
For full biography of Prof. Wang, please refer to: https://homes.luddy.indiana.edu/xw7/?
Moderator
For full biography of Prof. Chen, please refer to: https://datascience.hku.hk/people/ho-chen/
For information, please contact:
Email: datascience@hku.hk
- November 1, 2024
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