Privacy-Preserving Techniques for Prompt Engineering in Large Language Models

Concetti Chiave

Prompting large language models with sensitive data poses significant privacy risks. This survey systematically reviews various techniques, including sanitization, obfuscation, encryption, and differential privacy, to mitigate these privacy concerns during prompting.

Sintesi

This survey provides a comprehensive overview of privacy-preserving techniques for prompt engineering in large language models (LLMs). The key points are:

Prompting LLMs with sensitive data can lead to privacy leaks, as the prompts and generated outputs may contain private information. This has become a major obstacle in the widespread usage of LLMs.

The survey categorizes the privacy-preserving methods into four main groups: non-differential privacy (non-DP), local differential privacy (LDP), global differential privacy (GDP), and other scenarios.

Non-DP methods include sanitization, ensembling, obfuscation, lattice, and encryption techniques to protect the privacy of prompts and/or LLM responses.

LDP methods perturb the text prompts, soft prompts, or demonstration examples at the user side before sending them to the untrusted LLM server. This includes techniques that operate at the word, sentence, or document level.

GDP methods leverage sample and aggregate, PATE-based, and DP synthetic data generation to protect the privacy of demonstration examples used in prompts.

Other methods include techniques that perform data augmentation, client data protection via federated learning, and placing demonstration examples directly on the LLM side.

The survey also discusses the available resources for privacy-preserving prompting and the limitations of the existing frameworks, highlighting promising future research directions.

Statistiche

"The recent advancements in pre-trained language models (PLMs) have demonstrated significant strides across a wide array of natural language processing (NLP) tasks."
"The term large language models (LLMs) is employed to delineate these large-sized PLMs."
"Prompting, an emerging capability of LLMs, can generate anticipated outputs for a given query when provided with natural language instructions and/or demonstration examples, without necessitating updates to the model parameters."
"In-context learning (ICL) is another form of prompting proposed along with GPT-3 and includes a few demonstration examples in the prompt."

Citazioni

"Privacy concerns have become a major obstacle in its widespread usage."
"Addressing the privacy challenges associated with ICL specifically, as well as prompting in general, is a matter of urgency."
"Sensitive information of this nature could potentially be accessed by either an untrusted LLM server or an adversarial entity capable of bypassing the API provided by the LLM service provider."

Approfondimenti chiave tratti da

Privacy Preserving Prompt Engineering

by Kennedy Edem... alle arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06001.pdf

Domande più approfondite

How can the privacy-preserving techniques reviewed in this survey be extended to handle more complex prompting scenarios, such as chain-of-thought or planning-based prompts?

In more complex prompting scenarios like chain-of-thought or planning-based prompts, privacy-preserving techniques can be extended by incorporating differential privacy mechanisms at different levels of the prompt generation process. For chain-of-thought prompts, where intermediate reasoning steps are involved, differential privacy can be applied to each step of the reasoning process to ensure that the privacy of the data used in each step is protected. This can involve perturbing the representations or embeddings of the data at each step to prevent the leakage of sensitive information. Additionally, for planning-based prompts that decompose tasks into sub-tasks, differential privacy can be applied to each sub-task to protect the privacy of the data involved in each sub-task. By applying differential privacy techniques at each stage of the prompt generation process, more complex prompting scenarios can be handled while ensuring the privacy of the data used in the prompts.

What are the potential trade-offs between the level of privacy protection and the performance or utility of the prompting-based LLM applications?

There are several potential trade-offs between the level of privacy protection and the performance or utility of prompting-based LLM applications. One trade-off is the impact on the accuracy and effectiveness of the LLM model. Introducing privacy-preserving techniques such as differential privacy or encryption can add noise or distortion to the data, which may reduce the accuracy of the model's predictions. This trade-off between privacy and accuracy needs to be carefully balanced to ensure that the level of privacy protection does not significantly compromise the performance of the LLM application.
Another trade-off is the computational overhead associated with privacy-preserving techniques. Implementing complex privacy protection mechanisms can increase the computational resources required for training and inference, potentially slowing down the model and affecting its utility in real-time applications. Balancing the level of privacy protection with the computational efficiency of the LLM is crucial to maintain the performance and utility of the application.
Furthermore, there may be trade-offs in terms of the interpretability and explainability of the LLM model. Some privacy-preserving techniques may make it more challenging to interpret the model's decisions or provide explanations for its predictions, which can impact the utility of the model in certain applications where transparency is essential.

How can the privacy-preserving prompting techniques be integrated with other privacy-enhancing technologies, such as secure multi-party computation or homomorphic encryption, to provide more comprehensive privacy guarantees?

To enhance privacy guarantees, privacy-preserving prompting techniques can be integrated with other privacy-enhancing technologies such as secure multi-party computation (MPC) or homomorphic encryption.
Secure multi-party computation allows multiple parties to jointly compute a function over their private inputs without revealing the inputs to each other. By incorporating MPC into privacy-preserving prompting, multiple parties can collaborate to generate prompts without exposing their private data to each other or to the LLM server. This ensures that the privacy of the data used in the prompts is maintained throughout the collaboration process.
Homomorphic encryption enables computations to be performed on encrypted data without decrypting it, preserving the privacy of the data throughout the computation process. By applying homomorphic encryption to the data used in prompts and responses, the privacy of the data can be protected even when processed by the LLM. This integration ensures end-to-end privacy protection from the generation of prompts to the generation of responses.
By combining privacy-preserving prompting techniques with secure multi-party computation and homomorphic encryption, a more comprehensive privacy framework can be established, providing stronger privacy guarantees for LLM applications while maintaining the utility and performance of the models.