PIMENTO: A Privacy Framework for Querying Text

2026-09-13

AI Summary

Pimento框架解决了非结构化文本分析中的隐私保护问题，通过将自然语言转换为关系数据库，并结合差分隐私和上下文完整性提供正式的隐私保障。

Abstract

Currently, there are two state-of-the-art, complementary privacy guarantees: contextual integrity (CI) for what may flow, and differential privacy (DP) for what may be inferred. Yet neither maps cleanly onto natural language, leaving existing approaches unable to provide these guarantees for analytics over unstructured text. We address this gap with Pimento, a framework that takes three forms of natural language: text corpus, queries, and privacy policies; and grounds them into a relational database, creating a common substrate on which both guarantees can be enforced formally. With this design, we not only provide end to end privacy guarantees, but also improvement to utility through three key contributions: DP aware Text-to-SQL, which searches for correct queries requiring the least DP noise; CI aware Text-to-SQL, which compiles natural language policies into executable CI rules over the database; and a new privacy definition we call contextual differential privacy, which redefines the traditional DP neighborhood under CI, and yields a tighter smooth sensitivity bound. Across new benchmarks, Pimento selects the best query in 75.3% of cases (upto +45 points over baselines) and achieves zero leakage under correct policy grounding. To our knowledge, Pimento is the first framework to provide formal privacy guarantees for natural language analytics under CI, DP, and their composition.

Problem

• Privacy
• Natural Language
• Contextual Integrity
• Differential Privacy
• Text Analytics

Innovation

• Pimento
• contextual integrity
• differential privacy
• Text-to-SQL
• contextual differential privacy