# SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining

arXiv: https://arxiv.org/abs/1805.00904 · HTML: https://mlsanity.com/paper/1805.00904/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 21 of 21)

- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Differentially Private Language Models for Secure Data Sharing](https://mlsanity.com/paper/2210.13918/) (2022)
- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [Natural Language Understanding with Privacy-Preserving BERT](https://mlsanity.com/paper/2104.07504/) (2021)
- [A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage](https://mlsanity.com/paper/2504.21035/) (2025)
- [Guiding Text-to-Text Privatization by Syntax](https://mlsanity.com/paper/2306.01471/) (2023)
- [A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off](https://mlsanity.com/paper/2404.03324/) (2024)
- [Differential Privacy in Natural Language Processing: The Story So Far](https://mlsanity.com/paper/2208.08140/) (2022)
- [1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy](https://mlsanity.com/paper/2405.01678/) (2024)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text](https://mlsanity.com/paper/2405.19831/) (2024)
- [Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting](https://mlsanity.com/paper/2503.22379/) (2025)
- [On the privacy-utility trade-off in differentially private hierarchical text classification](https://mlsanity.com/paper/2103.02895/) (2021)
- [Investigating User Perspectives on Differentially Private Text Privatization](https://mlsanity.com/paper/2503.09338/) (2025)
- [I still know it's you! On Challenges in Anonymizing Source Code](https://mlsanity.com/paper/2208.12553/) (2022)
- [Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification](https://mlsanity.com/paper/2405.01097/) (2024)
- [Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming](https://mlsanity.com/paper/2509.16915/) (2025)
- [LLM Anonymization Against Agentic Re-Identification](https://mlsanity.com/paper/2605.30848/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anonymous: Search logs can reveal sensitive details about a user, critical articles or messages about a company or government might have severe or fatal consequences for a critic, and negative feedback in customer surveys might negatively impact business relations if they are identified. Simply removing personally identifying information from a document is, however, insufficient to protect the writer's identity: Given some reference texts of suspect authors, so-called authorship attribution methods can reidentfy the author from the text itself. One of the most prominent models to represent documents in many common text mining and information retrieval tasks is the vector space model where each document is represented as a vector, typically containing its term frequencies or related quantities. We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors. We evaluate our method on an exemplary text classification task and demonstrate that it only has a low impact on its accuracy. In contrast, we show that our method strongly affects authorship attribution techniques to the level that they become infeasible with a much stronger decline in accuracy. Other than previous authorship obfuscation methods, our approach is the first that fulfills differential privacy and hence comes with a provable plausible deniability guarantee.