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.

分析可能包含个人信息的时间序列数据（尤其是在医疗领域中），存在严重的隐私风险。我们在现有的Membership Inference Attacks（MIA）技术基础上，探索了适用于时间序列模型的新特性，着重于数据的季节性和趋势成分。我们在健康领域的数据集上应用了这些技术，结果表明这些新特性提高了MIAs在识别成员身份上的有效性，增进了对医疗数据应用中隐私风险的理解。

针对时间序列模型的成员推断攻击