  Analyzing time-series data that may contain personal information,
particularly in the medical field, presents serious privacy concerns. Sensitive
health data from patients is often used to train machine-learning models for
diagnostics and ongoing care. Assessing the privacy risk of such models is
crucial to making knowledgeable decisions on whether to use a model in
production, share it with third parties, or deploy it in patients homes.
Membership Inference Attacks (MIA) are a key method for this kind of
evaluation, however time-series prediction models have not been thoroughly
studied in this context. We explore existing MIA techniques on time-series
models, and introduce new features, focusing on the seasonality and trend
components of the data. Seasonality is estimated using a multivariate Fourier
transform, and a low-degree polynomial is used to approximate trends. We
applied these techniques to various types of time-series models, using datasets
from the health domain. Our results demonstrate that these new features enhance
the effectiveness of MIAs in identifying membership, improving the
understanding of privacy risks in medical data applications.
