5 papers
Disparate Impact in Synthetic Data Generation
Paul Andrey, Michaël Perrot, Batiste Le Bars +1
We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups.…
Distilling Human-Aligned Privacy Sensitivity Assessment from Large Language Models
Gabriel Loiseau, Damien Sileo, Damien Riquet +2
Accurate privacy evaluation of textual data remains a critical challenge in privacy-preserving natural language processing. Recent work has shown that large language models (LLMs)…
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization
Gabriel Loiseau, Damien Sileo, Damien Riquet +2
Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privac…
Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization
Gabriel Loiseau, Damien Sileo, Damien Riquet +2
Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-o…
TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization Methods
Gabriel Loiseau, Damien Sileo, Damien Riquet +2
Authorship obfuscation aims to disguise the identity of an author within a text by altering the writing style, vocabulary, syntax, and other linguistic features associated with the…