CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training
Training models on a carefully chosen portion of data rather than the full dataset is now a standard preprocess for modern ML. From vision coreset selection to large-scale filtering in language models, it enables scalability with minimal utility loss. A common intuition is that training on fewer samples should also reduce privacy risks. In this paper, we challenge this assumption. We show that subset training is not privacy free: the very choices of which data are included or excluded can introduce new privacy surface and leak more sensitive information. Such information can be captured by adversaries either through side-channel metadata from the subset selection process or via the output...
| Authors | Qi Li, Cheng-Long Wang, Yinzhi Cao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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