A Review on Penetration Testing for Privacy of Deep Learning Models
Abstract
As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are eff... | Find, read and cite all the research you need on Tech Science Press
Fields of Study & Subjects
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data
Software Testing and Debugging Techniques
Publication details
- Journal / source
- Computers, materials & continua/Computers, materials & continua (Print)
- Volume
- 87
- Issue
- 2
- Pages
- 1–10
- Publication date
- 1 January 2026
- Language
- EN