Pith. sign in

REVIEW 3 cited by

IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.02956 v2 pith:POCH57KE submitted 2024-07-03 cs.CR cs.AIcs.CLcs.LG

Ahmed Frikha , Nassim Walha , Krishna Kanth Nakka , Ricardo Mendes , Xue Jiang , Xuebing Zhou This is my paper · ORCID
classification cs.CRcs.AIcs.CLcs.LG
keywords privatetextanonymizationattributeincognitextattributesleakageutility
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics. We propose IncogniText, a technique that anonymizes the text to mislead a potential adversary into predicting a wrong private attribute value. Our empirical evaluation shows a reduction of private attribute leakage by more than 90% across 8 different private attributes. Finally, we demonstrate the maturity of IncogniText for real-world applications by distilling its anonymization capability into a set of LoRA parameters associated with an on-device model. Our results show the possibility of reducing privacy leakage by more than half with limited impact on utility.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation

    cs.CR 2026-04 unverdicted novelty 6.0 of 10

    RPSG generates realistic synthetic replicas of private text by combining private seeds with public LLMs and a formal differential privacy mechanism in candidate selection.

  2. Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models

    cs.CR 2025-07 conditional novelty 5.0 of 10

    HomeLLaMA fine-tunes a local 8B language model on cloud-generated smart home data to provide personalized, privacy-preserving device control, with optional obfuscated cloud queries when the local model falls short.

  3. A Survey: Towards Privacy and Security in Mobile Large Language Models

    cs.CR 2025-09 conditional

    A survey of privacy and security challenges for mobile large language models, summarizing known attack types and defenses without introducing new results.

Pith tools