Membership Inference Attacks Against Time-Series Models
This work addresses privacy concerns for patients and healthcare providers using time-series models, but it is incremental as it builds on existing MIA techniques.
The paper tackled the problem of assessing privacy risks in time-series models, particularly in medical applications, by exploring and enhancing Membership Inference Attacks (MIA) with new features based on seasonality and trend components, resulting in improved effectiveness in identifying membership.
Analyzing time-series data that contains 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 or share it with third parties. 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.