Title: Reversible Watermark-Guided Data Obfuscation to Prevent Exploitation by Unauthorized Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Privacy, natural language watermarking, data obfuscation

Abstract: The widespread availability of ``free'' data on the Internet has been key to recent advances in deep learning. However, it also raises serious concerns about the unauthorized use of public data to train commercial models. This paper investigates a critical issue in the domain of data privacy and deep learning: \textit{Can data be made unexploitable to unauthorized models while being recoverable for authorized models?} To address this issue, we propose Reversible Data Obfuscation, the first method that leverages watermark in a reversible manner to control data usability for target models. Specifically, we employ a gradient-based search (GBS) combined with constraint-guided obfuscation (CGO) strategy to select watermarkable tokens, generating watermarked obfuscated data that remains imperceptible to humans while preserving normal data utility. For unauthorized models, watermarked obfuscated data increases the discrepancy between predicted and ground-truth labels, thereby effectively degrading model performance. In contrast, authorized models can extract the watermark to recover the original high-quality data for effective training. Experimental results on classification tasks show that models fine-tuned on the watermarked obfuscated data experience severe performance degradation, with accuracy dropping below $50\%$. Moreover, the performance of unauthorized models degrades progressively with repeated embedding. The obfuscated data preserve high semantic similarity with the original data, and the embedded watermarks can be reliably extracted for ownership verification. Our work establishes an important step towards making data unexploitable to unauthorized models while remaining exploitable to authorized models.