mlsanityData to 7 Oct 2026

Membership Inference Attacks Against Time-Series Models

arXiv 2407.02870 ↗First posted 3 Jul 2024cs.LG cs.AIcited by 1

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 19 of its 36 references that are in the map. First posted on arXiv 3 Jul 2024.

Builds on

19 of 36 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation30 May
  2. 2024SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks13 Mar
  3. 2023Improved Membership Inference Attacks Against Language Classification Models11 Oct
  4. 2021Membership Inference Attacks From First Principles7 Dec

Other references · 15

  1. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  7. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  8. 2022Transformers in Time Series: A Surveycited by 489
  9. 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 239
  10. 2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 80
  11. 2019Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?cited by 68
  12. 2017Knock Knock, Who's There? Membership Inference on Aggregate Location Datacited by 63
3 more
  1. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  2. 2021NeuralProphet: Explainable Forecasting at Scalecited by 22
  3. 2024Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transformcited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inferencecited by 0