PRvL: Quantifying the Capabilities and Risks of Large Language Models
  for PII Redaction
Redacting Personally Identifiable Information (PII) from unstructured text is
critical for ensuring data privacy in regulated domains. While earlier
approaches have relied on rule-based systems and domain-specific Named Entity
Recognition (NER) models, these methods fail to generalize across formats and
contexts. Recent advances in Large Language Models (LLMs) offer a promising
alternative, yet the effect of architectural and training choices on redaction
performance remains underexplored. LLMs have demonstrated strong performance in
tasks that require contextual language understanding, including the redaction
of PII in free-form text. Prior work suggests that with appropriate adaptation,
LLMs can become effective contextual privacy learners. However, the
consequences of architectural and training choices for PII Redaction remain
underexplored. In this work, we present a comprehensive analysis of LLMs as
privacy-preserving PII Redaction systems. We evaluate a range of LLM
architectures and training strategies for their effectiveness in PII Redaction.
Our analysis measures redaction performance, semantic preservation, and PII
leakage, and compares these outcomes against latency and computational cost.
The results provide practical guidance for configuring LLM-based redactors that
are accurate, efficient, and privacy-aware. To support reproducibility and
real-world deployment, we release PRvL, an open-source suite of fine-tuned
models, and evaluation tools for general-purpose PII Redaction. PRvL is built
entirely on open-source LLMs and supports multiple inference settings for
flexibility and compliance. It is designed to be easily customized for
different domains and fully operable within secure, self-managed environments.
This enables data owners to perform redactions without relying on third-party
services or exposing sensitive content beyond their own infrastructure.
6895810a48b0ae5ca2710df2
Leon Garza
6895810a48b0ae5ca2710df3
Anantaa Kotal
6895810a48b0ae5ca2710df4
Aritran Piplai
6895810a48b0ae5ca2710df5
Lavanya Elluri
6895810a48b0ae5ca2710df6
Prajit Das
6895810a48b0ae5ca2710df7
Aman Chadha
claimed_verified
2025-08-08T16:19:25.097Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
PRvL: Quantifying the Capabilities and Risks of Large Language Models
  for PII Redaction
Redacting Personally Identifiable Information (PII) from unstructured text is
critical for ensuring data privacy in regulated domains. While earlier
approaches have relied on rule-based systems and domain-specific Named Entity
Recognition (NER) models, these methods fail to generalize across formats and
contexts. Recent advances in Large Language Models (LLMs) offer a promising
alternative, yet the effect of architectural and training choices on redaction
performance remains underexplored. LLMs have demonstrated strong performance in
tasks that require contextual language understanding, including the redaction
of PII in free-form text. Prior work suggests that with appropriate adaptation,
LLMs can become effective contextual privacy learners. However, the
consequences of architectural and training choices for PII Redaction remain
underexplored. In this work, we present a comprehensive analysis of LLMs as
privacy-preserving PII Redaction systems. We evaluate a range of LLM
architectures and training strategies for their effectiveness in PII Redaction.
Our analysis measures redaction performance, semantic preservation, and PII
leakage, and compares these outcomes against latency and computational cost.
The results provide practical guidance for configuring LLM-based redactors that
are accurate, efficient, and privacy-aware. To support reproducibility and
real-world deployment, we release PRvL, an open-source suite of fine-tuned
models, and evaluation tools for general-purpose PII Redaction. PRvL is built
entirely on open-source LLMs and supports multiple inference settings for
flexibility and compliance. It is designed to be easily customized for
different domains and fully operable within secure, self-managed environments.
This enables data owners to perform redactions without relying on third-party
services or exposing sensitive content beyond their own infrastructure.
A comprehensive analysis of Large Language Models for PII redaction evaluates various architectures and training strategies, providing guidance for accurate, efficient, and privacy-aware redaction systems.
Large Language Models Named Entity Recognition PII Redaction semantic preservation PII leakage latency computational cost fine-tuned models evaluation tools open-source LLMs inference settings secure self-managed environments