Qian Wang
Scholar

Qian Wang

Google Scholar ID: CD7ybnAAAAAJ
IEEE Fellow, School of Cyber Science and Engineering, Wuhan University
AI system securitywireless system securityapplied cryptography
Citations & Impact
All-time
Citations
18,308
 
H-index
64
 
i10-index
191
 
Publications
20
 
Co-authors
4
list available
Contact
No contact links provided.
Resume (English only)
Academic Achievements
  • Oct. 2025: One paper accepted by IEEE/ACM ToN
  • Oct. 2025: One paper accepted by IEEE TPAMI
  • Sep. 2025: One paper accepted by IEEE TPAMI
  • Sep. 2025: One paper accepted by IEEE TDSC
  • Sep. 2025: One paper accepted by IEEE/ACM ToN
  • Jun. 2025: One paper accepted by IEEE ICCV 2025
  • May. 2025: One paper accepted by ACM CCS 2025
  • May. 2025: One paper accepted by ACL 2025
  • Mar. 2025: One paper accepted by IEEE TDSC
  • Mar. 2025: One paper accepted by IEEE TIFS
  • Jan. 2025: Two papers accepted by USENIX Security 2025
  • Dec. 2024: One paper accepted by SCIENCE CHINA Information Sciences
  • Dec. 2024: One paper accepted by IEEE TIFS
  • Nov. 2024: One paper accepted by Journal of Software (软件学报)
  • Oct. 2024: One paper accepted by NDSS 2025
  • Oct. 2024: One paper accepted by IEEE TIFS
  • Sep. 2024: One paper accepted by NeurIPS 2024
  • May. 2024: One paper accepted by ACM CCS 2024
  • Apr. 2024: One paper accepted by ACM CCS 2024
Background
  • Research at the Network Information System Security & Privacy (NIS&P) Lab focuses on three areas: cloud computing security, wireless system security, and big data and AI security.
  • In cloud security, the lab aims to deliver secure, privacy-preserving, usable, scalable, and high-performance data outsourcing services, including privacy-preserving search over large-scale encrypted data, secure cloud storage auditing with strong correctness guarantees, and secure outsourced computation for large-scale engineering problems.
  • In wireless system security, the lab leverages physical-layer (PHY) characteristics to build robust and secure communication channels, with a focus on short-range communications among smart devices and joint performance-security designs for off-the-shelf smartphones.
  • In big data and AI security, the lab investigates privacy-preserving machine learning (especially deep learning) across data training, classification, and feature extraction, and also designs sophisticated attacks against AI systems such as face recognition, voice recognition, and autonomous driving.
  • The research is interdisciplinary, integrating advanced cryptographic tools, differential privacy, machine/deep learning, and signal processing techniques.