RedacBench: Can AI Erase Your Secrets?
- URL: http://arxiv.org/abs/2603.20208v1
- Date: Mon, 02 Mar 2026 06:29:49 GMT
- Title: RedacBench: Can AI Erase Your Secrets?
- Abstract summary: We introduce RedacBench, a benchmark for evaluating policy-conditioned redaction across domains and strategies.<n>RedacBench measures a model's ability to selectively remove policy-violating information while preserving the original semantics.<n>We quantify performance using 8,053 annotated propositions that capture all inferable information in each text.
- Score: 48.280759014096354
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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/.
Related papers
- RedactionBench [0.0]
Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII)<n>RedactionBench is a benchmark comprising 200 diverse documents across 11 domains.<n>R-Score is a novel character-level metric that treats semantically similar redactions equally.
arXiv Detail & Related papers (2026-06-17T07:51:56Z) - Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search [60.197239728279534]
Large language models (LLMs) in cloud-based services have raised significant privacy concerns.<n>Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility.<n>We propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness.
arXiv Detail & Related papers (2025-09-25T07:23:52Z) - Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning [58.16354555208417]
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively.<n>The lack of a Unified Face Attack Detection model to simultaneously handle attacks in these two categories is mainly attributed to two factors.<n>We present a novel Visual-Language Model-based Hierarchical Prompt Tuning Framework that adaptively explores multiple classification criteria from different semantic spaces.
arXiv Detail & Related papers (2025-05-19T16:35:45Z) - Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks [1.6427658855248815]
In this study, we reproduce GEIA's findings across various neural sentence embedding models.<n>We propose a simple yet effective method without any modification to the attacker's architecture proposed in GEIA.<n>Our findings indicate that following our approach, an adversary party can recover meaningful sensitive information related to the pre-training knowledge of the popular models used for creating sentence embeddings.
arXiv Detail & Related papers (2025-04-23T10:50:23Z) - Truthful Text Sanitization Guided by Inference Attacks [3.3802914883339557]
We introduce a novel text sanitization method based on generalizations that subsume the semantic content of the original text spans.<n>The approach relies on the use of instruction-tuned large language models (LLMs) and is divided into two stages.<n>Results on the Text Anonymization Benchmark show that the proposed approach, implemented with Mistral 7B Instruct, leads to enhanced utility.
arXiv Detail & Related papers (2024-12-17T14:07:01Z) - DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts [11.883785681042593]
DePrompt is a desensitization protection and effectiveness evaluation framework for prompt.
We integrate contextual attributes to define privacy types, achieving high-precision PII entity identification.
Our framework is adaptable to prompts and can be extended to text usability-dependent scenarios.
arXiv Detail & Related papers (2024-08-16T02:38:25Z) - Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
arXiv Detail & Related papers (2024-07-16T14:28:56Z) - IDT: Dual-Task Adversarial Attacks for Privacy Protection [8.312362092693377]
Methods to protect privacy can involve using representations inside models that are not to detect sensitive attributes.
We propose IDT, a method that analyses predictions made by auxiliary and interpretable models to identify which tokens are important to change.
We evaluate different datasets for NLP suitable for different tasks.
arXiv Detail & Related papers (2024-06-28T04:14:35Z) - Text Sanitization Beyond Specific Domains: Zero-Shot Redaction &
Substitution with Large Language Models [0.0]
We present a zero-shot text sanitization technique that detects and substitutes potentially sensitive information using Large Language Models.
Our evaluation shows that our method excels at protecting privacy while maintaining text coherence and contextual information.
arXiv Detail & Related papers (2023-11-16T18:42:37Z) - JPAVE: A Generation and Classification-based Model for Joint Product
Attribute Prediction and Value Extraction [59.94977231327573]
We propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE.
Two variants of our model are designed for open-world and closed-world scenarios.
Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines.
arXiv Detail & Related papers (2023-11-07T18:36:16Z) - FLIRT: Feedback Loop In-context Red Teaming [79.63896510559357]
We propose an automatic red teaming framework that evaluates a given black-box model and exposes its vulnerabilities.
Our framework uses in-context learning in a feedback loop to red team models and trigger them into unsafe content generation.
arXiv Detail & Related papers (2023-08-08T14:03:08Z) - Verifying the Robustness of Automatic Credibility Assessment [50.55687778699995]
We show that meaning-preserving changes in input text can mislead the models.
We also introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
Our experimental results show that modern large language models are often more vulnerable to attacks than previous, smaller solutions.
arXiv Detail & Related papers (2023-03-14T16:11:47Z) - Semantics-Preserved Distortion for Personal Privacy Protection in Information Management [65.08939490413037]
This paper suggests a linguistically-grounded approach to distort texts while maintaining semantic integrity.
We present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach.
We also explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization.
arXiv Detail & Related papers (2022-01-04T04:01:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.