LatticeGen: A Cooperative Framework Which Hides Generated Text in A Lattice For Privacy-Aware Generation on Cloud

Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Fatemehsadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov

Keywords: Privacy and Security, Large Language Model, Lattice, Generative Models, NLP

Abstract: In the current user-server interaction paradigm of prompted generation with large language model (LLM) on cloud, the server fully controls the generation process, which leaves zero option 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 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 attack 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).

Primary Area: generative models

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Submission Number: 1537