RedacBench: Can AI Erase Your Secrets?
arXiv:2603.20208v1 Announce Type: new Abstract: Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security. However, existing benchmarks for redaction typically focus on predefined categories of data such as personally identifiable information (PII) or evaluate specific techniques like masking. To address this limitation, we introduce RedacBench, a comprehensive benchmark for evaluating policy-conditioned redaction across domains and strategies. Constructed from 514 human-authored texts spanning individual, corporate, and government sources, paired with 187 security policies, RedacBench measures a model's ability to selectively remove policy-violating information while preserving the original semantics. We quantify performance using 8,053 annotated propositions that capture all inferable information in each text. This enables assessment of both security -- the remo
arXiv:2603.20208v1 Announce Type: new Abstract: Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security. However, existing benchmarks for redaction typically focus on predefined categories of data such as personally identifiable information (PII) or evaluate specific techniques like masking. To address this limitation, we introduce RedacBench, a comprehensive benchmark for evaluating policy-conditioned redaction across domains and strategies. Constructed from 514 human-authored texts spanning individual, corporate, and government sources, paired with 187 security policies, RedacBench measures a model's ability to selectively remove policy-violating information while preserving the original semantics. We quantify performance using 8,053 annotated propositions that capture all inferable information in each text. This enables assessment of both security -- the removal of sensitive propositions -- and utility -- the preservation of non-sensitive propositions. Experiments across multiple redaction strategies and state-of-the-art language models show that while more advanced models can improve security, preserving utility remains a challenge. To facilitate future research, we release RedacBench along with a web-based playground for dataset customization and evaluation. Available at https://hyunjunian.github.io/redaction-playground/.
Executive Summary
RedacBench introduces a novel benchmark for evaluating policy-conditioned redaction in unstructured text. This comprehensive framework assesses a model's ability to selectively remove sensitive information while preserving original semantics. The benchmark consists of 514 human-authored texts, 187 security policies, and 8,053 annotated propositions. Experiments demonstrate that advanced language models can improve security but struggle with preserving utility. The authors release RedacBench along with a web-based playground for dataset customization and evaluation, facilitating future research in this critical area. This study underscores the need for more effective redaction techniques to ensure data security.
Key Points
- ▸ RedacBench is a comprehensive benchmark for policy-conditioned redaction in unstructured text.
- ▸ The benchmark evaluates a model's ability to selectively remove sensitive information while preserving original semantics.
- ▸ Experiments demonstrate the efficacy of advanced language models in improving security but highlight the challenge of preserving utility.
Merits
Strength
RedacBench provides a standardized framework for evaluating redaction techniques, enabling more effective comparison of model performance across different domains and strategies.
Demerits
Limitation
The benchmark's reliance on human-authored texts and security policies may limit its generalizability to real-world scenarios, where data sources and policies can be diverse and complex.
Expert Commentary
The development of RedacBench is a significant step forward in the field of natural language processing and data security. By providing a comprehensive framework for evaluating policy-conditioned redaction, the authors have created a valuable tool for researchers and practitioners alike. However, as with any benchmark, there are limitations to consider. The reliance on human-authored texts and security policies may limit the generalizability of the results to real-world scenarios. Nevertheless, the insights gained from this study have significant implications for policy-making in the area of data protection. As the demand for data security continues to grow, the development of more effective redaction techniques will be critical. RedacBench provides a vital benchmark for evaluating and improving these techniques.
Recommendations
- ✓ Future research should focus on developing more advanced redaction techniques that can effectively preserve utility while improving security.
- ✓ The development of RedacBench should be expanded to include a broader range of data sources and security policies to enhance its generalizability.
Sources
Original: arXiv - cs.CL
