DPLongSyn: Differentially Private Longitudinal Image Synthesis

Longitudinal image data capture changes in the same subject over time, such as anatomical changes across medical scans of the same patient. Such data often contain sensitive individual information, motivating synthetic data release with formal differential privacy (DP) guarantees. However, existing DP image synthesis methods mainly focus on individual images, while longitudinal synthesis also requires coherent changes between observations that agree with the associated records. To this end, we introduce DPLongSyn, a DP longitudinal image synthesis framework that learns record-conditioned longitudinal transitions and uses them to synthesize follow-up images from public source images. DPLongSyn first learns a transition prior from public longitudinal pairs. It then uses private data to generate DP synthetic records describing subject attributes and longitudinal changes and to adapt the transition model using DP-SGD. At synthesis time, the adapted model generates a deformation conditioned on a public source image and a DP synthetic record, then applies it to produce the follow-up image. Experiments on three benchmarks show that DPLongSyn achieves better synthetic data quality, longitudinal consistency, and downstream utility than DP synthesis baselines. To the best of our knowledge, this is the first work to study DP longitudinal image synthesis.