arXiv:2510.01137v3 Announce Type: replace Abstract: Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent (DP-SGD) -- which clips per-sample gradients and adds calibrated Gaussian noise -- is the standard tool for formal privacy guarantees. Both theory and practice show that lower-rank models are better suited to DP training, a property especially relevant for LLMs, whose fine-tuning gradients exhibit a strong low-rank structure. Methods such as DP-LoRA exploit this by restricting updates to a low-ra
The increasing deployment of large language models in sensitive applications necessitates robust privacy assurances, driving immediate research into fine-tuning methods like LoRA.
This development addresses a critical barrier to LLM adoption in industries handling confidential data, potentially expanding their use cases significantly.
The ability to fine-tune LLMs with stronger privacy guarantees reduces the risk of data leakage, making these powerful models more viable for private-sector and government applications.
- · Enterprises handling sensitive data
- · Private LLM providers
- · AI-powered healthcare solutions
- · Government agencies
- · Actors who rely on data exfiltration from LLMs
- · Generic public LLM fine-tuning services
Wider adoption of LLMs in highly regulated and sensitive sectors due to improved privacy.
Increased demand for specialized private fine-tuning solutions and services.
Enhanced trust in AI systems leading to faster integration into critical information infrastructures.
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Read at arXiv cs.LG