ConfusionPrompt: Practical Private Inference for Online Large Language Models

Researchers introduce ConfusionPrompt, a privacy framework for large language models that decomposes user prompts into smaller sub-prompts mixed with pseudo-prompts before sending to cloud servers. The method protects user privacy while maintaining higher utility than existing perturbation-based approaches and works with existing black-box LLMs without modification.

ConfusionPrompt addresses a critical vulnerability in how modern LLMs operate: every user query sent to cloud-based services reveals sensitive information to service providers. This research proposes a decomposition-based obfuscation strategy that fundamentally changes the privacy model for online AI inference. By splitting prompts and introducing decoy queries, the framework ensures that servers cannot easily reconstruct the original user intent, though computational overhead is managed through careful prompt design.

The technical contribution extends beyond simple input perturbation by introducing a formal privacy model with parameters (λ, μ, ρ) that mathematically defines privacy guarantees. This represents progress in quantifying privacy-utility tradeoffs in LLM contexts, a space currently dominated by ad-hoc solutions. The approach integrates with existing production LLMs without requiring architectural changes, making adoption feasible compared to running local open-source models that sacrifice quality.

For AI service providers and users, this research signals growing demand for privacy-preserving inference mechanisms. As regulatory scrutiny on data handling increases globally, organizations deploying LLMs face pressure to minimize data exposure. ConfusionPrompt demonstrates that privacy protection doesn't require abandoning state-of-the-art models, reducing the quality penalty that previously made privacy a reluctant choice.

The framework's real-world impact depends on adoption barriers: users must tolerate increased latency from multiple server requests, and providers must accept serving decoy queries that consume computational resources. Enterprise deployments handling sensitive information—legal, medical, financial—represent the primary market. Future work likely involves optimizing decomposition strategies and exploring cryptographic alternatives that provide stronger privacy guarantees.

• →ConfusionPrompt protects LLM user privacy by decomposing prompts and mixing in pseudo-prompts before cloud submission.
• →The method works seamlessly with existing black-box LLMs without requiring model retraining or architectural changes.
• →Formal privacy model (λ, μ, ρ) provides mathematical framework for quantifying privacy-utility tradeoffs in inference.
• →Achieves higher output quality than local open-source models while reducing memory consumption compared to alternatives.
• →Addresses enterprise demand for privacy-preserving AI inference in regulated industries handling sensitive data.