SelPE: Progressive Selection for Private Structured Text Synthesis

2026-06-21

AI Summary

This work addresses the challenge of generating high-quality synthetic structured text under strict privacy constraints, where real data are scarce and heavily regulated. Existing differentially private synthesis methods struggle to simultaneously preserve semantic fidelity and structural validity. To overcome this, the authors propose SelPE, a novel framework that concentrates the privacy budget on a multi-batch top-1 progressive selection process and decouples generation into two stages—semantic abstraction followed by structural realization. Candidate samples are evaluated using a multi-channel distance kernel, and diversity is enhanced through a non-private contrastive expansion mechanism that incurs no additional privacy cost. Experiments demonstrate that, under stringent differential privacy guarantees and limited sample sizes, SelPE significantly improves the structural validity, semantic fidelity, and downstream utility of the synthesized data.

Abstract

Many data-driven applications rely on structured textual records, such as clinical triage notes and financial transaction logs, for downstream learning and decision-making. In privacy-sensitive domains, access to such records is strictly regulated, often resulting in only a small number of available private examples for model development and analysis. Yet existing differential privacy data synthesis methods fall short: tabular techniques cannot faithfully model free-form text, while text-based approaches often break structural constraints. We propose SelPE, a selection-guided progressive evolution framework for small-sample private structured text synthesis. Rather than relying on noisy aggregation or private model training, SelPE concentrates privacy budget on a sequence of multi-batch top-1 selections, enabling efficient guidance under tight privacy constraints. To support faithful and valid synthesis, SelPE decouples semantic abstraction from schema realization via a two-stage generation pipeline, and evaluates candidates using a multi-channel distance kernel that jointly models textual, categorical, and numeric fields in their native representations. A non-private contrastive expansion mechanism further promotes diversity without incurring additional privacy cost. Extensive Experiments demonstrate that SelPE consistently improves structural validity, fidelity, and downstream utility under strict differential privacy budgets, particularly in low-data regimes.

Problem

• differential privacy
• structured text synthesis
• small-sample
• privacy-preserving
• data synthesis

Innovation

• differential privacy
• structured text synthesis
• progressive selection
• two-stage generation
• multi-channel distance kernel