Dr. Hafiz Imtiaz

As data grows in areas like healthcare, finance, and security, it offers many benefits but also raises serious privacy concerns because personal identity can often be inferred even from anonymized data. Existing machine‑learning and data‑sharing methods struggle to protect privacy when dealing with high‑dimensional data. To address this, we propose a new data‑publishing method called DP‑CDA. It creates synthetic data by carefully mixing sensitive data within each class and adding controlled randomness to guarantee strong privacy. Our analysis shows that DP‑CDA offers better privacy protection than existing methods while keeping the data more useful. This work led to this Security and Privacy publication.