Text Sanitization Beyond Specific Domains: Zero-Shot Redaction & Substitution with Large Language Models

• URL: http://arxiv.org/abs/2311.10785v1
• Date: Thu, 16 Nov 2023 18:42:37 GMT
• Title: Text Sanitization Beyond Specific Domains: Zero-Shot Redaction & Substitution with Large Language Models
• Authors: Federico Albanese and Daniel Ciolek and Nicolas D'Ippolito
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of information systems, text sanitization techniques are used to identify and remove sensitive data to comply with security and regulatory requirements. Even though many methods for privacy preservation have been proposed, most of them are focused on the detection of entities from specific domains (e.g., credit card numbers, social security numbers), lacking generality and requiring customization for each desirable domain. Moreover, removing words is, in general, a drastic measure, as it can degrade text coherence and contextual information. Less severe measures include substituting a word for a safe alternative, yet it can be challenging to automatically find meaningful substitutions. 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, preserving data utility for downstream tasks.

Related papers

• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• 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)
• NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human [55.20137833039499]
  We suggest sanitizing sensitive text using two common strategies used by humans. We curate the first corpus, coined NAP2, through both crowdsourcing and the use of large language models.
  arXiv  Detail & Related papers  (2024-06-06T05:07:44Z)
• Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
  We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context. Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
  arXiv  Detail & Related papers  (2023-07-25T20:24:22Z)
• 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)
• Differential Privacy for Text Analytics via Natural Text Sanitization [44.95170585853761]
  This paper takes a direct approach to text sanitization. Our insight is to consider both sensitivity and similarity via our new local DP notion. The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility.
  arXiv  Detail & Related papers  (2021-06-02T15:15:10Z)
• MOST: A Multi-Oriented Scene Text Detector with Localization Refinement [67.35280008722255]
  We propose a new algorithm for scene text detection, which puts forward a set of strategies to significantly improve the quality of text localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed to dynamically adjust the receptive fields of features. A Position-Aware Non-Maximum Suppression (PA-NMS) module is devised to exclude unreliable ones.
  arXiv  Detail & Related papers  (2021-04-02T14:34:41Z)
• Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding [80.3811072650087]
  We study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training. AWT is the first end-to-end model to hide data in text by automatically learning -- without ground truth -- word substitutions along with their locations.
  arXiv  Detail & Related papers  (2020-09-07T11:01:24Z)
• Sensitive Information Detection: Recursive Neural Networks for Encoding Context [0.20305676256390928]
  Leak of sensitive information can potentially be very costly. We show that simplistic, brittle rule sets for detecting sensitive information only find a small fraction of the actual sensitive information. We develop a novel family of sensitive information detection approaches which only assumes access to labeled examples.
  arXiv  Detail & Related papers  (2020-08-25T07:49:46Z)
• Privacy Guarantees for De-identifying Text Transformations [17.636430224292866]
  We derive formal privacy guarantees for text transformation-based de-identification methods on the basis of Differential Privacy. We compare a simple redact approach with more sophisticated word-by-word replacement using deep learning models on multiple natural language understanding tasks. We find that only word-by-word replacement is robust against performance drops in various tasks.
  arXiv  Detail & Related papers  (2020-08-07T12:06:42Z)

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.