arXiv:2606.05004v1 Announce Type: cross Abstract: With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue. Existing privacy-preserving inference methods sacrifice either utility or efficiency, and often require model-specific modifications that limit their compatibility. In this paper, we propose SharedRequest, a model-agnostic framework for privacy-preserving LLM inference that reformulates privacy protection at the batch level rather than the individual-prompt level. The key idea is to obs
As LLMs become ubiquitous, public concerns and regulatory pressures around prompt privacy are escalating, driving innovation in solutions that balance utility and security.
Protecting user prompt privacy is crucial for broader LLM adoption and trust, directly impacting market receptivity and regulatory frameworks for AI systems.
The proposed 'SharedRequest' framework suggests a new model-agnostic approach to LLM privacy, potentially enabling more secure and efficient inference across diverse models without sacrificing performance.
- · LLM developers
- · Enterprises adopting LLMs
- · Privacy-focused AI users
- · Cloud AI service providers
- · Adversarial data miners
- · Less privacy-focused LLM solutions
Increased user trust in LLM applications due to enhanced privacy safeguards.
Accelerated integration of LLMs into sensitive industries like healthcare and finance where data privacy is paramount.
Potential for privacy-preserving AI to become a standard feature, influencing competitive dynamics and regulatory mandates across the AI industry.
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Read at arXiv cs.AI