# Protecting Vulnerable Voices: Synthetic Dataset Generation for Self-Disclosure Detection

arXiv: https://arxiv.org/abs/2507.22930 · HTML: https://mlsanity.com/paper/2507.22930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Privacy-preserving tabular data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Privacy-preserving tabular data synthesis](https://mlsanity.com/synth/privacy-preserving-tabular-data-synthesis/)

## Builds on (most cited first, 11 of 11)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Beyond Memorization: Violating Privacy Via Inference with Large Language Models](https://mlsanity.com/paper/2310.07298/) (2023)
- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Fine-tuning Large Language Models with Sequential Instructions](https://mlsanity.com/paper/2403.07794/) (2024)
- [A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning](https://mlsanity.com/paper/2105.09198/) (2021)
- [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022)
- [Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation](https://mlsanity.com/paper/2402.06811/) (2024)
- [Unpacking the Layers: Exploring Self-Disclosure Norms, Engagement Dynamics, and Privacy Implications](https://mlsanity.com/paper/2502.10701/) (2025)
- [Revealing Cumulative Risks in Online Personal Information: A Data Narrative Study](https://mlsanity.com/paper/2204.01826/) (2022)

## Built on by (most cited first, 3 of 3)

- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [CAPID: Context-Aware PII Detection for Question-Answering Systems](https://mlsanity.com/paper/2602.10074/) (2026)
- [What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media](https://mlsanity.com/paper/2606.06784/) (2026)

## Abstract (arXiv)

Social platforms such as Reddit have a network of communities of shared interests, with a prevalence of posts and comments from which one can infer users' Personal Information Identifiers (PIIs). While such self-disclosures can lead to rewarding social interactions, they pose privacy risks and the threat of online harms. Research into the identification and retrieval of such risky self-disclosures of PIIs is hampered by the lack of open-source labeled datasets. To foster reproducible research into PII-revealing text detection, we develop a novel methodology to create synthetic equivalents of PII-revealing data that can be safely shared. Our contributions include creating a taxonomy of 19 PII-revealing categories for vulnerable populations and the creation and release of a synthetic PII-labeled multi-text span dataset generated from 3 text generation Large Language Models (LLMs), Llama2-7B, Llama3-8B, and zephyr-7b-beta, with sequential instruction prompting to resemble the original Reddit posts. The utility of our methodology to generate this synthetic dataset is evaluated with three metrics: First, we require reproducibility equivalence, i.e., results from training a model on the synthetic data should be comparable to those obtained by training the same models on the original posts. Second, we require that the synthetic data be unlinkable to the original users, through common mechanisms such as Google Search. Third, we wish to ensure that the synthetic data be indistinguishable from the original, i.e., trained humans should not be able to tell them apart. We release our dataset and code at https://netsys.surrey.ac.uk/datasets/synthetic-self-disclosure/ to foster reproducible research into PII privacy risks in online social media.