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This paper addresses the ethical concerns arising from the use ofunauthorized public data in deep learning models and proposes a novel solution.Specifically, building on the work of Huang et al. (2021), we extend theirbi-level optimization approach to generate unlearnable text using agradient-based search technique. However, although effective, this approachfaces practical limitations, including the requirement of batches of instancesand model architecture knowledge that is not readily accessible to ordinaryusers with limited access to their own data. Furthermore, even withsemantic-preserving constraints, unlearnable noise can alter the text'ssemantics. To address these challenges, we extract simple patterns fromunlearnable text produced by bi-level optimization and demonstrate that thedata remains unlearnable for unknown models. Additionally, these patterns arenot instance- or dataset-specific, allowing users to readily apply them to textclassification and question-answering tasks, even if only a small proportion ofusers implement them on their public content. We also open-source codes togenerate unlearnable text and assess unlearnable noise to benefit the publicand future studies.

Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data