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Daniel Samira

2 accepted papers

2026

Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs

ICLR 2026poster

Large language models (LLMs) are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information (PII) in a highly structured and explicit format. As a result, privacy risks arise, since sensitive records can be inadvertently retained by the…

Cited by 0SourceScholar
2025

Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models

CVPR 2025poster

The proliferation of multi-modal generative models has introduced new privacy and security challenges, especially due to the risks of memorization and unintentional disclosure of sensitive information. This paper focuses on the vulnerability of multi-modal image captioning models to membership infer…

Cited by 0SourcePDFScholar