OpenAI Blog
Apr 22
Introducing OpenAI Privacy Filter
OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy
Model Card for OpenAI Privacy Filter
The OpenAI Privacy Filter is a 1.5‑billion‑parameter, bidirectional token‑classification model that detects and redacts personally identifiable information and secrets in unstructured text. It is built from an autoregressive checkpoint, converted into a banded‑attention classifier, and uses a constrained Viterbi decoder to produce coherent spans across eight privacy categories in a single forward pass. The model supports configurable precision‑recall tradeoffs, a 128,000‑token context window, and is designed for efficient local deployment and domain‑specific fine‑tuning as a data‑minimization component within layered privacy workflows.
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OpenAI Privacy Filter: A Cross-Lingual, Cross-Domain PII Evaluation Across 32 Benchmarks
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On the Robustness of Vision-Language Models in Zero-shot Privacy Classification
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arXiv AI
Aug 5
Evaluating OpenAI's Privacy Filter: Cross-Lingual, Cross-Domain PII Detection Across 42 Benchmarks
arXiv:2608. 02616v1 Announce Type: cross Abstract: We present the first independent, systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.
arXiv AI
Jun 9
Vision Language Model Helps Private Information De-Identification in Vision Data
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arXiv AI
Jul 2
NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
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arXiv Machine Learning
Aug 4
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
arXiv:2410. 06814v2 Announce Type: replace Abstract: Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model.
arXiv AI
Jun 4
SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models
arXiv:2606. 05004v1 Announce Type: cross Abstract: With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue.
arXiv AI
Aug 17
CutClean: Neural Network Pruning for Privacy-Preserving Inference
arXiv:2608. 13773v1 Announce Type: cross Abstract: Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns.
arXiv AI
Aug 20
Redakto - The Incognito Tab for LLMs
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arXiv AI
Sep 1
Defending Wearable VLMs Against Private Attribute Inference
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