A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Differentially private text obfuscation aims to perturb input texts with Differential Privacy (DP) guarantees, making private output texts quantifiably indistinguishable from originals. While word-level perturbation is intuitive, meaningful text privatization often occurs at the complete document level. This research systematically evaluates multiple text decomposition and budget distribution techniques for DP text obfuscation. It tests how different methods for chunking texts can be combined with techniques for allocating an overall ε privacy budget to these chunks. Experiments demonstrate that these design choices are crucial, as comparable privacy budgets can yield significantly different results based on the chosen methods. The study provides credible evidence for maximizing empirical trade-offs by optimizing DP obfuscation procedures.

Key takeaway

For AI Security Engineers implementing differentially private text obfuscation, your choice of text decomposition and ε budget distribution methods is paramount. You must systematically evaluate different chunking and budget allocation strategies, as even with identical privacy budgets, outcomes can vary significantly. Prioritize optimizing these procedures to maximize empirical trade-offs and ensure robust privacy guarantees without unnecessary utility loss.

Key insights

Optimizing text decomposition and ε budget distribution is critical for effective differentially private text obfuscation.

Principles

• Design choices in text decomposition and budget distribution significantly impact DP obfuscation outcomes.
• Comparable privacy budgets can yield vastly different results based on method selection.
• Optimizing DP obfuscation procedures can maximize empirical trade-offs.

In practice

• Carefully select text decomposition and ε budget allocation methods.
• Evaluate different chunking and budget distribution combinations for DP text obfuscation.

Topics

• Differential Privacy
• Text Obfuscation
• Privacy Budget Allocation
• Text Decomposition
• ε-privacy

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.