DP-TabICL: In-Context Learning with Differentially Private Tabular Data

핵심 개념

The author explores the use of differential privacy to protect tabular data in the context of in-context learning, proposing two frameworks - LDP-TabICL and GDP-TabICL - that offer privacy guarantees while maintaining performance.

초록

The content delves into the application of differential privacy mechanisms to safeguard tabular data used in in-context learning. It introduces two frameworks, LDP-TabICL and GDP-TabICL, evaluating their effectiveness on real-world datasets. The study highlights the importance of protecting sensitive information while maintaining model performance.
Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) without retraining. Tabular data serialization enables ICL with LLMs, but poses privacy risks due to leaked information. Differential privacy (DP) is proposed as a solution to protect tabular data used in ICL.
Two DP-based frameworks are introduced: Local Differentially Private Tabular-based In-Context Learning (LDP-TabICL) and Global Differentially Private Tabular-based In-Context Learning (GDP-TabICL). These frameworks aim to generate demonstration examples for ICL while preserving the privacy of underlying tabular datasets.
Evaluation on eight real-world tabular datasets shows that DP-based ICL can maintain data privacy while achieving comparable performance to non-DP baselines. The study emphasizes the need for privacy protection in machine learning applications involving sensitive data.
Key metrics or figures:

ϵ values: 1, 5, 10, 25, 50
Dataset sizes: adult (48842 rows), bank (45211 rows), blood (748 rows), calhousing (20640 rows), car (1728 rows), diabetes (768 rows), heart (918 rows), jungle (44819 rows)

통계

ϵ values: 1, 5, 10, 25, 50

인용구

"We propose LDP-TabICL for generating demonstration examples that have formal local DP guarantees for use in tabular data classification via ICL."
"Our evaluations show that DP-based ICL can protect the privacy of the underlying tabular data while achieving comparable performance to non-LLM baselines."

핵심 통찰 요약

DP-TabICL

by Alycia N. Ca... 게시일 arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05681.pdf

더 깊은 질문

How can prompt engineering be optimized to improve accuracy while maintaining data privacy

Prompt engineering can be optimized to improve accuracy while maintaining data privacy by carefully designing the prompts to provide relevant context for the large language model (LLM) without revealing sensitive information. Here are some strategies to achieve this optimization:

Balancing Information and Privacy: The prompts should strike a balance between providing sufficient information for the LLM to make accurate predictions and withholding sensitive details that could compromise data privacy.

Contextual Relevance: Ensure that the prompts are contextually relevant to the task at hand, aligning with the specific domain or dataset being used for in-context learning.

Noise Injection: Introduce controlled noise or perturbations into the prompt text itself, similar to how differential privacy mechanisms add noise to data, ensuring that even if an adversary gains access to prompted examples, they cannot extract private information directly from them.

Template Design: Develop standardized templates for crafting prompts that maintain consistency across different datasets and tasks while minimizing potential leakage of sensitive details.

Dynamic Prompt Generation: Implement dynamic prompt generation techniques based on real-time feedback from model performance, adjusting prompts iteratively based on accuracy metrics without compromising data privacy.

Privacy-Preserving Language Models: Explore advanced techniques such as federated learning or secure multi-party computation where multiple parties collaborate in training models without sharing raw data directly, thus preserving individual data privacy while improving overall model accuracy.

What are the implications of using differential privacy mechanisms on different types of datasets beyond tabular ones

The implications of using differential privacy mechanisms extend beyond tabular datasets and can impact various types of datasets in different ways:

Text Data: Differential privacy can safeguard textual content by adding noise during processing or analysis stages, protecting against unauthorized disclosure of personal information contained within text documents or messages.

Image Data: Differential privacy methods applied to image datasets involve perturbing pixel values or features extracted from images before analysis, ensuring individual-level details remain confidential while still allowing useful insights to be derived from visual data.

Genomic Data: In genomics research, differential privacy helps protect genetic information shared among researchers or stored in databases by obscuring specific genetic markers or sequences linked to individuals' identities.

Healthcare Data: Differential privacy plays a crucial role in healthcare settings by anonymizing patient records and medical histories when conducting analyses or sharing insights across institutions, preventing re-identification risks associated with sensitive health-related information.

How might advancements in differential privacy impact broader applications of large language models beyond in-context learning

Advancements in differential privacy have significant implications for broader applications of large language models (LLMs) beyond in-context learning:
1.Enhanced Privacy Protection: By integrating more robust differential privacy mechanisms into LLMs' training processes and inference tasks, organizations can ensure stronger safeguards against potential leaks of confidential information embedded within textual inputs processed by these models.
2Regulatory Compliance: With increasing regulatory scrutiny around user data protection (e.g., GDPR), adopting advanced differential privacy measures enables companies leveraging LLMs for diverse applications like chatbots or sentiment analysis tools comply with stringent regulations governing user confidentiality.
3Cross-Domain Collaboration: Leveraging differential private LLMs facilitates secure collaboration across industries where sharing proprietary text-based datasets is essential but requires strict confidentiality protocols; sectors like finance collaborating with tech firms benefit from enhanced security measures.
4Ethical AI Development: Advancements in applying differential private principles promote ethical AI development practices by prioritizing user consent and transparency regarding how their personal data is utilized within machine learning algorithms powered by large language models.