Data Privacy in AI: Protecting Sensitive Information

Exploring methods to maintain your data privacy when using AI tools, focusing on local LLMs and data obfuscation techniques

Data privacy is a critical concern when using AI tools, especially for businesses handling sensitive information. Two primary approaches can help maintain privacy:

Using a Local LLM
Employing a Local Obfuscator before using a Public LLM

Comparison of Approaches

Aspect	Local LLM	Local Obfuscator + Public LLM
Data Control	Complete	Partial
Internet Dependency	None	Required
Computational Resources	High	Low to Moderate
Capabilities	Limited	Extensive
Privacy Risk	Minimal	Low to Moderate
Implementation Complexity	High	Moderate
Customization	Highly Customizable	Limited Customization
Maintenance	Regular updates needed	Minimal maintenance

1. Local LLM Approach

Running a Large Language Model (LLM) locally ensures that sensitive data never leaves your system.

2. Local Obfuscator + Public LLM Approach

This method involves preprocessing data to remove or alter sensitive information before sending it to a public AI service.

Legal Considerations

When using AI tools, it's crucial to understand the data usage policies of different platforms:

ChatGPT (OpenAI)

OpenAI's policies state:

"OpenAI may collect Personal Information that is included in the input, file uploads, or feedback that you provide to their Services." [1]

"OpenAI's models may use the content that you provide to the Services to improve the model's accuracy and performance." [2]

GitHub Copilot

GitHub's approach differs for personal and business use:

"GitHub does not claim any ownership rights in the Suggestions provided by GitHub Copilot, and you retain ownership of Your Code." [3]

"Copilot for Business does not retain any Code Snippets Data." [4]

By understanding these approaches and policies, developers and businesses can make informed decisions about how to protect their sensitive information while leveraging AI tools.