Important Dates

March 1, 2022

Workshop Proposal Due

May 1, 2022

Paper Submission Deadline

June 10, 2022

Author Notification

June 25, 2022

Camera-ready and Registration

August 22-25, 2022

Conference Date


Title: Achieving Cloud Data Security and Privacy in Zero Trust Environments

Speaker:Robert H. Deng

Singapore Management University

About the Speaker

Robert H. DengRobert Deng is AXA Chair Professor of Cybersecurity, Director of the Secure Mobile Centre, and Deputy Dean for Faculty & Research, School of Computing and Information Systems, Singapore Management University. His research interests are in the areas of data security and privacy, network security, and applied cryptography. He received the Outstanding University Researcher Award from National University of Singapore, Lee Kuan Yew Fellowship for Research Excellence from SMU, and Asia-Pacific Information Security Leadership Achievements Community Service Star from International Information Systems Security Certification Consortium. He serves/served on the editorial boards of ACM Transactions on Privacy and Security, IEEE Security & Privacy, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, Journal of Computer Science and Technology, and Steering Committee Chair of the ACM Asia Conference on Computer and Communications Security. He is a Fellow of IEEE and Fellow of Academy of Engineering Singapore.


This talk will provide an overview on the design and implementation of a system for secure access, search, and computation of encrypted data in the cloud for enterprise users. The system is designed following the “zero trust” paradigm to protect data security and privacy even if cloud storage servers or user accounts are compromised. This is achieved using end-to-end (E2E) encryption in which encryption and decryption operations only take place at client devices. However, encryption must not hinder access, search and even computation of data by authorized users. There are numerous academic publications in this area and the choice of which cryptographic techniques to use could have significant impact on the system’s scalability and usability. We will share our experience in the design of the system architecture and selection of cryptographic techniques with a consideration to balance security, performance, and usability.

Title: Neurosymbolic Autonomy and the Quest for Smart(er) Decision-Making

Speaker:Alvaro Velasquez

Information Directorate of the Air Force Research Laboratory

About the Speaker

Robert H. DengAlvaro Velasquez leads the machine intelligence sub-portfolio of investments for the Information Directorate of the Air Force Research Laboratory (AFRL) in the United States. In this capacity, he manages and proposes new research directions and technology transitions for the Air Force in the fields of artificial intelligence and autonomous systems. This entails close collaboration with both the academic and private sectors. Alvaro received his PhD in Computer Science from the University of Central Florida and holds an interdisciplinary research record, including publications in artificial intelligence, combinatorial optimization, networking, cloud computing, and logic and circuit design. Alvaro is a recipient of numerous awards, including the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) award, the University of Central Florida 30 Under 30 award, and best paper and patent awards from AFRL. He serves as Associate Editor of IEEE Transactions on Artificial Intelligence and his research is currently funded by the Air Force Office of Scientific Research.


Neurosymbolic Artificial Intelligence has experienced a renaissance and gained much traction in recent years as a potential “third wave” of AI to follow the tremendously successful second wave underpinned by statistical deep learning. This seeks the integration of neural learning systems and formal symbolic reasoning for more efficient, robust, and explainable AI. Such an integration holds much promise in areas like reinforcement learning and planning, where tremendous progress has been made in recent years, including great feats like the defeat of the world Go champion and powerful agents for real-time strategy games. However, the tremendous success of autonomous decision-making has highlighted its own shortcomings when it comes to data limitations, robustness, and trust, among other things. This talk presents some of these challenges and opportunities facing the development of neurosymbolic autonomy, how this differs from conventional neurosymbolic AI problems like classification and natural language processing, and potential implications to facilitating the broader adoption of autonomous solutions.


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