AIにより推定されたラベル
データリークやモデルの問題に関する分析を反映した新規ラベル データプライバシー管理 LLM性能評価
※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract
Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe KV caches and hidden states, threatening prompt privacy for open-source models. Cryptographic protections such as MPC and FHE offer strong guarantees but remain one to two orders of magnitude too slow for interactive inference, while static obfuscation schemes break under multi-run statistical attacks once the model is known. We present GELO (Good-Enough LLM Obfuscation), a lightweight protocol for privacy-preserving inference that limits information leakage from untrusted accelerator observations by hiding hidden states with fresh, per-batch invertible mixing. For each offloaded projection, the TEE samples a random matrix A, forms U = AH, offloads U and weights W to the accelerator, and then applies A−1 on return, so that A−1((AH)W) = HW and outputs are unchanged. Because mixing is never reused across batches, the attacker faces only a single-batch blind source separation problem. We analyze information leakage and introduce two practical defenses: (i) non-orthogonal mixing to mask Gram matrices, and (ii) orthogonal mixing augmented with a small fraction of high-energy “shield” vectors that pollute higher-order statistics. On Llama-2 7B, GELO preserves float32 outputs exactly, closely matches low-precision baselines, offloads the dominant matrix multiplications with about 20-30