Rate My Professor Shujie Cui

Cui's research expertise encompasses applied cryptography, information security in cloud computing and distributed systems, trusted execution environments, privacy-preserving machine learning, data protection in various systems, and side-channel attacks. Her influential publications include "SGX-LKL: Securing the host OS interface for trusted execution" (2019, 153 citations), "Privacy‐preserving collaborative deep learning against leakage from gradient sharing" (2020, 51 citations), "Multi-CDN: Towards privacy in content delivery networks" (2018, 43 citations), "Towards blockchain-based scalable and trustworthy file sharing" (2018, 41 citations), "Scalable private decision tree evaluation with sublinear communication" (2022, 29 citations), "Privacy-preserving dynamic symmetric searchable encryption with controllable leakage" (2021, 23 citations), "Searchable encryption for conjunctive queries with extended forward and backward privacy" (2021, 23 citations), and recent contributions such as "CHIFRAUD: A Long-term Web Text Benchmark for Chinese Fraud Detection" (2025, COLING), "Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective" (2025, RecSys), and "More Practical Non-interactive Encrypted Conjunctive Search with Leakage and Storage Suppression" (2025, ProvSec). With over 450 citations on Google Scholar, her scholarship advances secure computing paradigms. Cui leads the Efficient Weighted Threshold Signature project as Primary Chief Investigator (2025-2026) and contributes as Associate Investigator to CSIRO's Privacy-Preserving Machine Learning: Technology Development and Adoption (2023-2027) and Quantum Information Technology: Industry Readiness & Applications (2023-2027). She supervises higher degree research students on topics including privacy-preserving machine unlearning, searchable encryption, and federated learning for NLP and medical AI.