This paper introduces a novel privacy-preservation framework named PFID for
LLMs that addresses critical privacy concerns by localizing user data through
model sharding and singular value decomposition. When users are interacting
with LLM systems, their prompts could be subject to being exposed to
eavesdroppers within or outside LLM system providers who are interested in
collecting users' input. In this work, we proposed a framework to camouflage
user input, so as to alleviate privacy issues. Our framework proposes to place
model shards on the client and the public server, we sent compressed hidden
states instead of prompts to and from servers. Clients have held back
information that can re-privatized the hidden states so that overall system
performance is comparable to traditional LLMs services. Our framework was
designed to be communication efficient, computation can be delegated to the
local client so that the server's computation burden can be lightened. We
conduct extensive experiments on machine translation tasks to verify our
framework's performance.

该论文介绍了一种名为 PFID 的新型隐私保护框架，通过分散模型和奇异值分解的方式，解决了 LLM 系统中关键的隐私问题。