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Talent Scout

D

Dong Yan

University of Chinese Academy of Sciences

J

Jian Liang

NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences

R

Ran He

NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences

T

Tieniu Tan

Nanjing University

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References (45)

[1]
A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models
2025Yanbo Wang, Yongcan Yu et al.
[2]
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
2025Yanbo Wang, Jiyang Guan et al.
[3]
Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications
2025Shubhi Asthana, Bing Zhang et al.
[4]
Language Models are Advanced Anonymizers
2025Robin Staab, Mark Vero et al.
[5]
Dual Frequency-Guided Spatiotemporal Feature Learning for Face Forgery Detection
2025Junxian Duan, Siyu Liu et al.
[6]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[7]
Qwen2.5 Technical Report
2024Qwen An Yang, Baosong Yang et al.
[8]
Data Defenses Against Large Language Models
2024William Agnew, Harry H. Jiang et al.
[9]
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
2024Xiaogeng Liu, Peiran Li et al.
[10]
Attention heads of large language models
2024Zifan Zheng, Yezhaohui Wang et al.
[11]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[12]
IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization
2024Ahmed Frikha, Nassim Walha et al.
[13]
A Synthetic Dataset for Personal Attribute Inference
2024Hanna Yukhymenko, Robin Staab et al.
[14]
Improved Techniques for Optimization-Based Jailbreaking on Large Language Models
2024Xiaojun Jia, Tianyu Pang et al.
[15]
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
2024Maksym Andriushchenko, Francesco Croce et al.
[16]
Identifying Semantic Induction Heads to Understand In-Context Learning
2024Jie Ren, Qipeng Guo et al.
[17]
Query-Based Adversarial Prompt Generation
2024Jonathan Hayase, Ema Borevkovic et al.
[18]
PAL: Proxy-Guided Black-Box Attack on Large Language Models
2024Chawin Sitawarin, Norman Mu et al.
[19]
COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
2024Xing-ming Guo, Fangxu Yu et al.
[20]
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
2024Andy Zhou, Bo Li et al.

Showing 20 of 45 references

Founder's Pitch

"TRACE-RPS provides comprehensive privacy defense against attribute inference in LLMs by combining fine-grained anonymization with inference-prevention optimization."

Security and Privacy in LLMsScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

3/4 signals

7.5

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Why It Matters

The paper addresses a critical privacy concern in LLMs by preventing models from inferring sensitive user attributes from seemingly innocuous text, which is crucial for maintaining user privacy in the age of large-scale AI deployments.

Product Angle

Market a software tool or browser extension that uses TRACE-RPS to automatically anonymize text before it is shared on public platforms, ensuring compliance with privacy regulations.

Disruption

This technology could replace current coarse-grained anonymization tools and enhance existing privacy solutions by offering a more precise and adaptive method for protecting user data.

Product Opportunity

As AI adoption increases in various sectors, the demand for privacy assurance tools grows. Potential customers include tech companies, financial institutions, and healthcare providers concerned about data privacy and compliance.

Use Case Idea

Integrate TRACE-RPS as a privacy filter tool for enterprises using LLMs to process customer data, offering GDPR-compliant solutions for preventing unauthorized personal attribute inference.

Science

The paper introduces TRACE, a framework that uses attention mechanisms to identify and anonymize sensitive textual elements, and RPS, which optimizes text suffixes to prevent attribute inference by guiding models into refusal behaviors.

Method & Eval

The approach was tested on LLMs including Llama2, GPT-3.5-Turbo, and showed a reduction in attribute inference accuracy from 50% to below 5%, indicating significant performance improvement over existing methods.

Caveats

The effectiveness of RPS might be limited on models where the inner workings are inaccessible (i.e., closed-source models). The anonymization might alter text semantics in unpredictable ways, potentially affecting user trust.

Author Intelligence

Dong Yan

University of Chinese Academy of Sciences
yandong2025@ia.ac.cn

Jian Liang

NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences
liangjian92@gmail.com

Ran He

NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences

Tieniu Tan

Nanjing University