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Original Paper: https://arxiv.org/abs/2312.03724
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
Summary Notes
Revolutionizing Privacy in AI: Introducing DP-OPT
In the rapidly evolving world of technology, Large Language Models (LLMs) like GPT-3 have become fundamental in driving innovation across various applications.
However, the integration of these models, especially in enterprise solutions, has raised serious concerns about data privacy and the protection of intellectual property.
The development of DP-OPT (Differentially-Private Offsite Prompt Tuning) is a game-changer in this context, aiming to ensure that the advancements in AI do not compromise data confidentiality.
Facing the Privacy Hurdle
Prompt engineering is a technique used to fine-tune LLMs to generate specific outputs by feeding them carefully designed prompts.
While efficient, this process can inadvertently expose sensitive data to cloud-hosted LLMs, posing a risk to privacy and confidentiality. Traditional prompt tuning methods are not fully equipped to address these concerns.
Introducing DP-OPT: A Privacy-First Approach
DP-OPT stands out by enabling the creation of prompts on the client side, using a mechanism that ensures privacy without diminishing the LLMs' effectiveness. Here are its main benefits:
- Ensures Data Confidentiality: By generating prompts locally, sensitive information is kept secure.
- Protects Intellectual Property: It safeguards the cloud model's IP, fostering a safer collaboration environment.
- Maintains Performance: DP-OPT preserves the utility and accuracy of LLMs, proving effective in applications like sentiment analysis.
How DP-OPT Works
DP-OPT balances privacy with performance through a technique that involves local prompt tuning and the generation of prompts that are differentially private.
This approach effectively protects private data while still leveraging the power of cloud-based LLMs. Its versatility and adaptability have been proven, making it suitable for various applications.
Proven Effectiveness
Empirical studies, particularly in sentiment classification, have shown that DP-OPT achieves an impressive balance between privacy protection and utility. This evidence supports its potential as a valuable tool for organizations that prioritize data confidentiality.
Future Applications
The DP-OPT framework is particularly promising for industries such as finance, healthcare, and law, where data privacy is crucial. Its adoption can drive innovation while ensuring sensitive information remains protected.
Conclusion: Paving the Way for Privacy-Conscious AI
DP-OPT introduces a new paradigm in AI development, where privacy and performance coexist. As the field of AI advances, the principles underlying DP-OPT will be critical in guiding the development of ethical and responsible technologies.
For AI engineers in enterprise environments, embracing DP-OPT not only means leading in innovation but also championing a future where digital advancements are secure and privacy-respecting.
Acknowledgments
The creation of DP-OPT is a result of global collaborative efforts, highlighting the collective commitment to advancing AI technology with a focus on privacy. This achievement paves the way for a future where AI is not only powerful but also trusted and inclusive.
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