In this area, we study security and privacy issues from two perspectives. On one hand, we study how to employ AI techniques to improve system, network, and application security and privacy. On the other hand, we study the security and privacy of AI techniques/systems themselves. Some previous and ongoing research includes: (1) Adversarial Learning, in domains of image, audio, text, etc.; (2) Machine Learning Security and Privacy; (3) AI-aided Vulnerability Mining; (4) Smart Fuzzing; and (5) Secure Multi-Party Computing and Learning.
In this area, we study the privacy issues and privacy-preserving techniques for systems, networks, and data. Typically, problems could range from protecting system parameters to protecting users’ Personal Identifiable Information. The commonly employed techniques include applied crypto techniques, probability and statistics, graph theory, and optimization. Some previous and ongoing research includes: (1) Graph (Complex Data) Privacy; (2) Anonymization and De-anonymization; (3) Differential Privacy; and (4) Web Privacy.
Big Data Analysis
In this area, we focus on mining knowledge from massive data and understanding the evolution and characteristics of interested systems. Some previous and ongoing research includes: (1) Social Network Computing and Analytics and (2) Graph and Network Embedding.