Prune the Leaky Neurons: Mitigating Private Data Leakage in Fine-Tuned LLMs via Joint-Embedding-Guided Pruning
Fine-tuning large language models (LLMs) on proprietary records enables domain adaptation but introduces two challenges: (i) privacy leakage of memorized private data, such as personally identifiable information (PII), and (ii) balancing private data removal with utility preservation.
ProofPaper ↗
Key points
- We propose JEPA-Prune, a post-hoc neuron pruning framework guided by a model trained exclusively on PII-free views.
- Alongside the fine-tuned private model, JEPA-Prune, trains a public model on surrogate and masked views of the same records using a joint-embedding predictive architecture (JEPA), capturing domain knowledge without identifiers.
- A cross-model representation probe quantifies deviations between private representations of leaked records and this PII-free reference.
- Experiments show that pruning just 1.6% of neurons reduces PII inference accuracy by up to 7.4x and PII extraction recall by up to 13.9x, while keeping unseen-text perplexity within 16% of that of the unpruned model for most LLMs. In contrast, privacy-neuron editing increases perplexity by at least 26x.
Sources (1)
- [1]Prune the Leaky Neurons: Mitigating Private Data Leakage in Fine-Tuned LLMs via Joint-Embedding-Guided PruningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 01:09 AM
Fine-tuning large language models (LLMs) on proprietary records enables domain adaptation but introduces two challenges: (i) privacy leakage of memorized private data, such as personally identifiable information (PII), and (ii) balancing private data removal with utility preservation.
We propose JEPA-Prune, a post-hoc neuron pruning framework guided by a model trained exclusively on PII-free views.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 15, 2026Designing Transformers for Continuous Diffusion Language Models
- Oct 15, 2026Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
- Oct 15, 2026NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
- Oct 15, 2026Multimodal AI Detection for Math-Heavy Scientific Text
- Oct 14, 2026Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets
- Oct 14, 2026Gray-Box Hallucination Detection in LLMs with Token Identities