# A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism

arXiv: https://arxiv.org/abs/2403.07283 · HTML: https://mlsanity.com/paper/2403.07283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Large Language Models Can Be Strong Differentially Private Learners](https://mlsanity.com/paper/2110.05679/) (2021)
- [Differentially Private Fine-tuning of Language Models](https://mlsanity.com/paper/2110.06500/) (2021)
- [MPCFormer: fast, performant and private Transformer inference with MPC](https://mlsanity.com/paper/2211.01452/) (2022)
- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption](https://mlsanity.com/paper/2206.00216/) (2022)
- [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://mlsanity.com/paper/2307.12533/) (2023)
- [nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data](https://mlsanity.com/paper/1810.10121/) (2018)

## Built on by (most cited first, 1 of 1)

- [LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication](https://mlsanity.com/paper/2609.23193/) (2026)

## Abstract (arXiv)

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.