Privacy-Preserving Language Model Inference with Instance Obfuscation
Yixiang Yao Fei Wang Srivatsan Ravi Muhao Chen
Language Models as a Service (LMaaS) offers convenient access for developers
and researchers to perform inference using pre-trained language models.
Nonetheless, the input data and the inference results containing private
information are exposed as plaintext during the service call, leading to
privacy issues. Recent studies have started tackling the privacy issue by
transforming input data into privacy-preserving representation from the
user-end with the techniques such as noise addition and content perturbation,
while the exploration of inference result protection, namely decision privacy,
is still a blank page. In order to maintain the black-box manner of LMaaS,
conducting data privacy protection, especially for the decision, is a
challenging task because the process has to be seamless to the models and
accompanied by limited communication and computation overhead. We thus propose
Instance-Obfuscated Inference (IOI) method, which focuses on addressing the
decision privacy issue of natural language understanding tasks in their
complete life-cycle. Besides, we conduct comprehensive experiments to evaluate
the performance as well as the privacy-protection strength of the proposed
method on various benchmarking tasks.