mlsanityData to 7 Oct 2026

Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch

arXiv 2602.03183 ↗First posted 3 Feb 2026cs.CL cs.AIcited by 3

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Abstract (arXiv)

Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. This challenge is becoming increasingly urgent as modern AI agents--such as OpenClaw and Gemini Agent--are granted persistent access to highly sensitive personal information. To tackle this longstanding bottleneck and the rising risks, we present Privasis (i.e., privacy oasis), the first million-scale fully synthetic dataset entirely built from scratch--an expansive reservoir of texts with rich and diverse private information--designed to broaden and accelerate research in areas where processing sensitive social data is inevitable. Compared to existing datasets, Privasis, comprising 1.4 million records, offers orders-of-magnitude larger scale with quality, and far greater diversity across various document types, including medical history, legal documents, financial records, calendars, and text messages with a total of 55.1 million annotated attributes such as ethnicity, date of birth, workplace, etc. We leverage Privasis to construct a parallel corpus for text sanitization with our pipeline that decomposes texts and applies targeted sanitization. Our compact sanitization models (<=4B) trained on this dataset outperform state-of-the-art large language models, such as GPT-5 and Qwen-3 235B. We plan to release data, models, and code to accelerate future research on privacy-sensitive domains and agents.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 38 of its 52 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

38 of 52 references.

References · 38

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  5. 2024Phi-4 Technical Reportcited by 675
  6. 2023Textbooks Are All You Needcited by 561
  7. 2025LIMO: Less is More for Reasoningcited by 488
  8. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  9. 2021Efficient Attentions for Long Document Summarizationcited by 342
  10. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  11. 2023Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theorycited by 218
  12. 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 166
26 more
  1. 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 161
  2. 2022Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipecited by 108
  3. 2022The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymizationcited by 94
  4. 2024AirGapAgent: Protecting Privacy-Conscious Conversational Agentscited by 90
  5. 2024Differentially Private Synthetic Data via Foundation Model APIs 2: Textcited by 74
  6. 2022Differentially Private Language Models for Secure Data Sharingcited by 68
  7. 2023Differentially Private Synthetic Data via Foundation Model APIs 1: Imagescited by 63
  8. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
  9. 2024PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensemblescited by 54
  10. 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 50
  11. 2023Reducing Privacy Risks in Online Self-Disclosures with Language Modelscited by 47
  12. 2025Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoningcited by 38
  13. 2024A Synthetic Dataset for Personal Attribute Inferencecited by 35
  14. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Modelscited by 31
  15. 2024PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restorationcited by 27
  16. 2025Scaling Laws for Differentially Private Language Modelscited by 22
  17. 2024Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbotscited by 16
  18. 2025A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakagecited by 15
  19. 2024NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Humancited by 8
  20. 2025PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redactioncited by 7
  21. 2024Information-Theoretic Distillation for Reference-less Summarizationcited by 6
  22. 2025PANORAMA: A synthetic PII-laced dataset for studying sensitive data memorization in LLMscited by 6
  23. 2025Automated Privacy Information Annotation in Large Language Model Interactionscited by 6
  24. 2023Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysiscited by 4
  25. 2025Can Large Language Models Really Recognize Your Name?cited by 4
  26. 2025PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026PrivacyAlign: Contextual Privacy Alignment for LLM Agentscited by 2
  2. 2026Opal: Private Memory for Personal AIcited by 1
  3. 2026PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversationscited by 0