Enterprise AI Research Analysis
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However, this raises concerns about sensitive data leakage. This paper proposes PrivTune, an efficient and privacy-preserving fine-tuning framework via Split Learning (SL).
PrivTune: Enhanced LLM Privacy & Utility
PrivTune introduces a novel device-cloud collaboration framework for LLM fine-tuning, meticulously balancing robust privacy protection with minimal impact on model utility and performance.
- Leverages Split Learning (SL) for device-cloud collaboration.
- Injects crafted, importance-aware noise into token representations.
- Optimizes noise vector for defense-utility balance.
- Reduces attack success rate to 10% with minimal utility drop (3.33%).
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Attack Success Rate Reduction (EIA)
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Utility Drop (max)
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Performance Improvement (vs. SOTA)
Deep Analysis & Enterprise Applications
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Methodology
Privacy Mechanisms
Experimental Results
Core PrivTune Approach
PrivTune operates by splitting the LLM into a bottom model (on device) and a top model (cloud). Intermediate token representations are perturbed on the device before transmission to the cloud. This perturbation is guided by an optimization problem to balance privacy and utility, ensuring tokens resemble n-hop indirect neighbors while preserving semantic information. A token importance-aware dx-Privacy mechanism scales noise based on token sensitivity.
Enterprise Process Flow
User device computes intermediate representation (h)
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PrivTune injects crafted dx-Privacy noise (p)
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Perturbed representation (h+p) transmitted to cloud
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Cloud completes forward pass & fine-tuning (LoRA)
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Cloud sends model output/gradients back to device
10%
Target Attack Success Rate (AIAs/EIAs)
dx-Privacy & Optimization
PrivTune formalizes noise design as an optimization problem (OPT-2, reformulated to OPT-3 for efficiency) to minimize similarity between semantically distant tokens and maximize intra-class cluster distance. It also enforces utility constraints like prediction consistency, semantic preservation, and distributional validity. The dx-Privacy mechanism, tailored for continuous embedding spaces, injects noise whose mean is set by the optimal noise vector and scaled by token importance scores.
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Performance Benchmarking
Experiments on five datasets (classification/generation) against six attacks (embedding inversion, attribute inference) demonstrate PrivTune's superior privacy-utility trade-off. Using RoBERTa on SST, PrivTune achieves 10% ASR with only 3.33% UP drop, outperforming SOTA baselines. It also shows efficiency in computational and communication overhead.
Case Study: SST Dataset (RoBERTa Model)
On the Stanford Sentiment Treebank (SST) dataset, PrivTune significantly reduced the Attack Success Rate (ASR) to 10%. This was achieved with a minimal utility performance drop of only 3.33%, showcasing its effectiveness in balancing strong privacy protection with high model utility. This outcome surpassed all state-of-the-art baselines evaluated.
- ASR reduced to 10%
- Utility drop of 3.33%
- Outperforms SOTA baselines
92.53%
Utility Performance (%) on SST (RoBERTa) at 10% ASR
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Your PrivTune Implementation Roadmap
Our structured approach ensures a seamless integration of PrivTune into your existing LLM workflows.
Initial Assessment & Strategy
Evaluate current LLM usage, identify sensitive data, and define privacy requirements. Develop a tailored PrivTune implementation strategy.
Technical Integration & Fine-Tuning Setup
Deploy bottom models to edge devices, configure cloud-side top models, and integrate PrivTune's noise injection and optimization modules.
Privacy-Utility Calibration & Testing
Perform iterative fine-tuning with sample data, calibrate dx-Privacy parameters for optimal balance, and rigorously test against various inference attacks.
Deployment & Monitoring
Roll out PrivTune-enabled LLM services. Continuously monitor performance, privacy metrics, and adapt to evolving threats with dynamic adjustments.
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