A Framework for Cost-Effective and Self-Adaptive LLM Shaking and
  Recovery Mechanism
Zhiyu Chen Yu Li Suochao Zhang Jingbo Zhou Jiwen Zhou Chenfu Bao Dianhai Yu
As Large Language Models (LLMs) gain great success in real-world
applications, an increasing number of users are seeking to develop and deploy
their customized LLMs through cloud services. Nonetheless, in some specific
domains, there are still concerns regarding cost and trade-offs between privacy
issues and accuracy. In this study, we introduce a cost-effective and
self-adaptive LLM shaking tuning and recovery mechanism, named CypherTalk. With
carefully designed horizontal and vertical shaking operators, we can achieve
comparable accuracy results with SOTA privacy-preserving LLM schemes using
Cryptography-based or Differential Privacy-based methods. Experiments also show
that with the CypherTalk framework, users can achieve reliable accuracy when
using optimized shaking operator settings. To our best knowledge, this is the
first work that considers cost, and trade-off between model utility and privacy
in LLM scenarios.