Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting

• URL: http://arxiv.org/abs/2503.22379v1
• Date: Fri, 28 Mar 2025 12:33:46 GMT
• Title: Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting
• Abstract summary: We construct and evaluate a toolkit of linguistics- and NLP-based methods used to allocate a privacy budget to constituent tokens in a text document.<n>Our work highlights the intricacies of text privatization with DP, and furthermore, it calls for further work on finding more efficient ways to maximize the privatization benefits offered by DP in text rewriting.
• Score: 3.0177210416625124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of $\textit{Differentially Private Text Rewriting}$ is a class of text privatization techniques in which (sensitive) input textual documents are $\textit{rewritten}$ under Differential Privacy (DP) guarantees. The motivation behind such methods is to hide both explicit and implicit identifiers that could be contained in text, while still retaining the semantic meaning of the original text, thus preserving utility. Recent years have seen an uptick in research output in this field, offering a diverse array of word-, sentence-, and document-level DP rewriting methods. Common to these methods is the selection of a privacy budget (i.e., the $\varepsilon$ parameter), which governs the degree to which a text is privatized. One major limitation of previous works, stemming directly from the unique structure of language itself, is the lack of consideration of $\textit{where}$ the privacy budget should be allocated, as not all aspects of language, and therefore text, are equally sensitive or personal. In this work, we are the first to address this shortcoming, asking the question of how a given privacy budget can be intelligently and sensibly distributed amongst a target document. We construct and evaluate a toolkit of linguistics- and NLP-based methods used to allocate a privacy budget to constituent tokens in a text document. In a series of privacy and utility experiments, we empirically demonstrate that given the same privacy budget, intelligent distribution leads to higher privacy levels and more positive trade-offs than a naive distribution of $\varepsilon$. Our work highlights the intricacies of text privatization with DP, and furthermore, it calls for further work on finding more efficient ways to maximize the privatization benefits offered by DP in text rewriting.

Related papers

• PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization [50.692938672750266]
  PrivBench is a benchmarking platform for researchers and practitioners working on text privatization.<n>PrivBench is free to use and openly accessible at https://privbench.com/.
  arXiv  Detail & Related papers  (2026-08-30T07:33:08Z)
• A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation [16.51235052027641]
  We show how different methods for chunking texts can be combined with techniques for allocating $varepsilon$ to these chunks.<n>Our experiments reveal that such design choices are very important, as even with comparable privacy budgets, significantly different results can occur based on which methods are chosen.
  arXiv  Detail & Related papers  (2026-05-01T20:00:14Z)
• Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search [60.197239728279534]
  Large language models (LLMs) in cloud-based services have raised significant privacy concerns.<n>Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility.<n>We propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness.
  arXiv  Detail & Related papers  (2025-09-25T07:23:52Z)
• Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees [18.487751624471777]
  We introduce DP-ST, which leverages semantic triples for neighborhood-aware private document generation under local DP guarantees.<n>Our method allows for coherent text generation even at lower $varepsilon$ values, while still balancing privacy and utility.
  arXiv  Detail & Related papers  (2025-08-28T12:59:01Z)
• DP-Fusion: Token-Level Differentially Private Inference for Large Language Models [51.71591819896191]
  Large language models (LLMs) do not preserve privacy at inference-time.<n> DP-Fusion provably bounds the influence a set of tokens in the context can have on the LLM's output.<n>We show that our method creates token-level provably privatized documents with substantially improved theoretical and empirical privacy.
  arXiv  Detail & Related papers  (2025-07-06T20:49:39Z)
• InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy [7.006059299522521]
  InvisibleInk is a scalable long-form text generation framework satisfying rigorous differential privacy guarantees.<n>We reduce the privacy cost by isolating and clipping only the sensitive information in the model logits.<n>We improve text quality by sampling from a small superset of the top-$k$ private tokens.
  arXiv  Detail & Related papers  (2025-06-30T18:00:41Z)
• Pr$εε$mpt: Sanitizing Sensitive Prompts for LLMs [49.84954577111077]
  Pr$epsilonepsilon$mpt is a novel system that implements a prompt sanitizer. We show that Pr$epsilonepsilon$mpt is a practical method to achieve meaningful privacy guarantees.
  arXiv  Detail & Related papers  (2025-04-07T14:52:40Z)
• Investigating User Perspectives on Differentially Private Text Privatization [81.59631769859004]
  This work investigates how factors of $textitscenario$, $textitdata sensitivity$, $textitmechanism type$, and $textitreason for data collection$ impact user preferences for text privatization.<n>We learn that while all these factors play a role in influencing privacy decisions, users are highly sensitive to the utility and coherence of the private output texts.
  arXiv  Detail & Related papers  (2025-03-12T12:33:20Z)
• Optimized Tradeoffs for Private Prediction with Majority Ensembling [59.99331405291337]
  We introduce the Data-dependent Randomized Response Majority (DaRRM) algorithm.<n>DaRRM is parameterized by a data-dependent noise function $gamma$, and enables efficient utility optimization over the class of all private algorithms.<n>We show that DaRRM provably enjoys a privacy gain of a factor of 2 over common baselines, with fixed utility.
  arXiv  Detail & Related papers  (2024-11-27T00:48:48Z)
• A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy [3.0177210416625124]
  Several word-level $textitMetric$ Differential Privacy approaches have been proposed. We devise a method where composed privatized outputs have higher semantic coherence and variable length. We evaluate our method in utility and privacy tests, which make a clear case for tokenization strategies beyond the word level.
  arXiv  Detail & Related papers  (2024-06-30T09:37:34Z)
• NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human [55.20137833039499]
  We suggest sanitizing sensitive text using two common strategies used by humans. We curate the first corpus, coined NAP2, through both crowdsourcing and the use of large language models.
  arXiv  Detail & Related papers  (2024-06-06T05:07:44Z)
• Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text [3.3916160303055567]
  We propose a simple post-processing method based on the goal of aligning rewritten texts with their original counterparts. Our results show that such an approach not only produces outputs that are more semantically reminiscent of the original inputs, but also texts which score on average better in empirical privacy evaluations.
  arXiv  Detail & Related papers  (2024-05-30T08:41:33Z)
• DP-BART for Privatized Text Rewriting under Local Differential Privacy [2.45626162429986]
  We propose a new system 'DP-BART' that largely outperforms existing LDP systems. Our approach uses a novel clipping method, iterative pruning, and further training of internal representations which drastically reduces the amount of noise required for DP guarantees.
  arXiv  Detail & Related papers  (2023-02-15T13:07:34Z)
• Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano [83.5933307263932]
  We study data reconstruction attacks for discrete data and analyze it under the framework of hypothesis testing. We show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $epsilon$ can be $O(log M)$ before the adversary gains significant inferential power.
  arXiv  Detail & Related papers  (2022-10-24T23:50:12Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent [69.14164921515949]
  We characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees.
  arXiv  Detail & Related papers  (2022-06-06T13:49:37Z)
• Sentence-level Privacy for Document Embeddings [25.779351166096255]
  We propose SentDP: pure local differential privacy at the sentence level for a single user document. Our experiments indicate that these private document embeddings are useful for downstream tasks like sentiment analysis and topic classification.
  arXiv  Detail & Related papers  (2022-05-10T00:19:35Z)
• Privacy Amplification via Shuffling for Linear Contextual Bandits [51.94904361874446]
  We study the contextual linear bandit problem with differential privacy (DP) We show that it is possible to achieve a privacy/utility trade-off between JDP and LDP by leveraging the shuffle model of privacy. Our result shows that it is possible to obtain a tradeoff between JDP and LDP by leveraging the shuffle model while preserving local privacy.
  arXiv  Detail & Related papers  (2021-12-11T15:23:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.