Stateful Exposure-Aware De-Identification for Multimodal Streaming Data
Abstract
Research implementation of an exposure-aware, adaptive PHI de-identification system for streaming multimodal data. The method models cumulative identity exposure over time and dynamically adjusts masking strength based on quantified re-identification risk. The framework supports cross-modal linkage modeling, structured audit logging, and reproducible privacy–utility evaluation in streaming machine learning environments.
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
Internet Traffic Analysis and Secure E-voting
Data Quality and Management
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 4 March 2026
- Language
- EN