  In the current user-server interaction paradigm of prompted generation with
large language models (LLM) on cloud, the server fully controls the generation
process, which leaves zero options for users who want to keep the generated
text to themselves. We propose LatticeGen, a cooperative framework in which the
server still handles most of the computation while the user controls the
sampling operation. The key idea is that the true generated sequence is mixed
with noise tokens by the user and hidden in a noised lattice. Considering
potential attacks from a hypothetically malicious server and how the user can
defend against it, we propose the repeated beam-search attack and the mixing
noise scheme. In our experiments we apply LatticeGen to protect both prompt and
generation. It is shown that while the noised lattice degrades generation
quality, LatticeGen successfully protects the true generation to a remarkable
degree under strong attacks (more than 50% of the semantic remains hidden as
measured by BERTScore).
