QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing
Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the underlying data source, such as the proportion of records associated with a particular gender, diagnosis, or political stance. Existing work has largely focused on property inference attacks that recover such global properties, while defenses for protecting these dataset-level secrets remain limited. Differential privacy, although effective for protecting individual records, provides only weak protection for aggregate properties. We propose Randomized Quantization for Text (QuanText), a training-free and large-language-model-agnostic data re...
| Authors | Shuaiqi Wang, Zinan Lin, Giulia Fanti |
|---|---|
| Primary category | cs.LG |
| Published | 16 September 2026 |
| Last updated at source | 18 September 2026 |
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