🤖 AI Summary
This work addresses the trilemma in large language model (LLM) inference—balancing privacy preservation, model utility, and computational efficiency—by proposing a lightweight client-side encryption framework. It introduces, for the first time, a formal definition of the “obfuscated semantic nullspace,” into which input embeddings are projected to achieve privacy without requiring post-processing. The method integrates user-key-driven random perturbation trajectories with geometry-aware noise injection in the latent space, enabling personalized privacy guarantees while maintaining efficient inference. Evaluated across twelve generative and classification benchmarks, the approach achieves state-of-the-art performance, substantially reducing attack success rates while preserving high model utility under stringent security constraints.
📝 Abstract
We propose Obfuscated Semantic Null space Injection for Privacy (OSNIP), a lightweight client-side encryption framework for privacy-preserving LLM inference. Generalizing the geometric intuition of linear kernels to the high-dimensional latent space of LLMs, we formally define the ``Obfuscated Semantic Null Space'', a high-dimensional regime that preserves semantic fidelity while enforcing near-orthogonality to the original embedding. By injecting perturbations that project the original embedding into this space, OSNIP ensures privacy without any post-processing. Furthermore, OSNIP employs a key-dependent stochastic mapping that synthesizes individualized perturbation trajectories unique to each user. Evaluations on 12 generative and classification benchmarks show that OSNIP achieves state-of-the-art performance, sharply reducing attack success rates while maintaining strong model utility under strict security constraints.