Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
<|reference_start|>Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack: This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an \emph{informed adversary} threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings.   We evaluate six popular dimensionality reduction techniques: PCA, sparse random projection (SRP), multidimensional scaling (MDS), Isomap, $t$-SNE, and UMAP. Using both MNIST and NIH Chest X-ray datasets, we perform a qualitative analysis to identify key factors affecting reconstruction quality. Furthermore, we assess the effectiveness of an additive noise mechanism in mitigating these reconstruction attacks.<|reference_end|>
@article{lumbut2024investigating,
    title={Investigating Privacy Leakage in Dimensionality Reduction Methods via
  Reconstruction Attack},
    author={Chayadon Lumbut, Donlapark Ponnoprat},
    journal={arXiv preprint arXiv:2408.17151},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17151},
    primaryClass={cs.CR cs.LG}
}