Tabular Health Data Under Attack: Benchmarking Privacy Risks and Defenses
| Title | Tabular Health Data Under Attack: Benchmarking Privacy Risks and Defenses |
|---|---|
| Summary | This thesis aims to investigate privacy attacks and defenses in tabular health data. |
| Keywords | |
| TimeFrame | Autumn25-Spring26 |
| References | [[References::[1] He, Z., Ouyang, C., Wen, L., Liu, C. and Moreira, C., 2025. TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data. arXiv preprint arXiv:2505.21027.
[2] Alshantti, A., Rasheed, A. and Westad, F., 2025. Privacy Re‐Identification Attacks on Tabular GANs. Security and Privacy, 8(1), p.e469.]] |
| Prerequisites | |
| Author | |
| Supervisor | Jens Lundström, Eric Järpe, Atiye Sadat Hashemi |
| Level | Master |
| Status | Open |
This thesis aims to investigate privacy attacks and defenses in tabular health data, focusing on understanding how sensitive information can be inferred from structured datasets and how modern privacy-preserving techniques can mitigate these risks. The project will involve studying and implementing state-of-the-art attack methods (e.g., membership and attribute inference) and defense mechanisms (e.g., differential privacy and adversarial noise injection) on benchmark datasets such as MIMIC-III, IV, which are commonly used in healthcare research. The goal is to provide a comprehensive evaluation framework for measuring privacy–utility trade-offs and to propose or refine novel defense approaches that enhance protection while maintaining analytical value in health-related tabular data.
