DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
- URL: http://arxiv.org/abs/2312.03724v2
- Date: Sun, 17 Mar 2024 23:16:41 GMT
- Title: DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
- Authors: Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang,
- Abstract summary: Large Language Models (LLMs) have emerged as dominant tools for various tasks.
However, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency on sensitive private information.
We present Differentially-Private Offsite Prompt Tuning (DP-OPT) to address this challenge.
- Score: 57.04801796205638
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency on sensitive private information. A practical solution is to host a local LLM and optimize a soft prompt privately using data. Yet, hosting a local model becomes problematic when model ownership is protected. Alternative methods, like sending data to the model's provider for training, intensify these privacy issues facing an untrusted provider. In this paper, we present a novel solution called Differentially-Private Offsite Prompt Tuning (DP-OPT) to address this challenge. Our approach involves tuning a discrete prompt on the client side and then applying it to the desired cloud models. We demonstrate that prompts suggested by LLMs themselves can be transferred without compromising performance significantly. To ensure that the prompts do not leak private information, we introduce the first private prompt generation mechanism, by a differentially-private (DP) ensemble of in-context learning with private demonstrations. With DP-OPT, generating privacy-preserving prompts by Vicuna-7b can yield competitive performance compared to non-private in-context learning on GPT3.5 or local private prompt tuning. Codes are available at https://github.com/VITA-Group/DP-OPT .
Related papers
- Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit.
We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
arXiv Detail & Related papers (2024-06-20T13:54:32Z) - Differentially Private Model-Based Offline Reinforcement Learning [51.1231068185106]
We introduce DP-MORL, an algorithm coming with differential privacy guarantees.
A private model of the environment is first learned from offline data.
We then use model-based policy optimization to derive a policy from the private model.
arXiv Detail & Related papers (2024-02-08T10:05:11Z) - Private Fine-tuning of Large Language Models with Zeroth-order
Optimization [54.24600476755372]
We introduce DP-ZO, a new method for fine-tuning large language models that preserves the privacy of training data by privatizing zeroth-order optimization.
We show that DP-ZO exhibits just $1.86%$ performance degradation due to privacy at $ (1,10-5)$-DP when fine-tuning OPT-66B on 1000 training samples from SQuAD.
arXiv Detail & Related papers (2024-01-09T03:53:59Z) - Large Language Models Can Be Good Privacy Protection Learners [53.07930843882592]
We introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning language models.
Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning.
In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model's knowledge.
arXiv Detail & Related papers (2023-10-03T22:37:01Z) - Can Language Models be Instructed to Protect Personal Information? [30.187731765653428]
We introduce PrivQA -- a benchmark to assess the privacy/utility trade-off when a model is instructed to protect specific categories of personal information in a simulated scenario.
We find that adversaries can easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs.
We believe PrivQA has the potential to support the development of new models with improved privacy protections, as well as the adversarial robustness of these protections.
arXiv Detail & Related papers (2023-10-03T17:30:33Z) - Privacy-Preserving Prompt Tuning for Large Language Model Services [16.589104544849743]
We propose a framework that provides privacy guarantees for Large Language Models (LLMs) services.
textscrapt adopts a local privacy setting, allowing users to privatize their data locally with local differential privacy.
Despite the simplicity of our framework, experiments show that RAPT achieves competitive performance across tasks while providing privacy guarantees against adversaries.
arXiv Detail & Related papers (2023-05-10T14:41:51Z) - Just Fine-tune Twice: Selective Differential Privacy for Large Language
Models [69.66654761324702]
We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models.
Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
arXiv Detail & Related papers (2022-04-15T22:36:55Z) - Selective Differential Privacy for Language Modeling [36.64464956102432]
Previous work has attempted to tackle this challenge by training RNN-based language models with differential privacy guarantees.
We propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data.
Experiments on both language modeling and dialog system building show that the proposed privacy-preserving mechanism achieves better utilities.
arXiv Detail & Related papers (2021-08-30T01:11:10Z) - Privacy-Adaptive BERT for Natural Language Understanding [20.821155542969947]
We study how to improve the effectiveness of NLU models under a Local Privacy setting using BERT.
We propose privacy-adaptive LM pretraining methods and demonstrate that they can significantly improve model performance on privatized text input.
arXiv Detail & Related papers (2021-04-15T15:01:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.