Implementing Data Protection When Building Private LLMs - Lanner Electronics | Network Appliance | uCPE SD-WAN| MEC Server | Intelligent Edge Appliance

Introduction

As generative AI models such as Large Language Models (LLMs) become integral to various industries, ensuring the privacy and security of sensitive data is critical. LLMs often interact with sensitive corporate or even personal data during training and inference, posing significant risks to privacy; such challenges must be addressed by de-identifying and securing sensitive information throughout the AI lifecycle.

Challenges

LLMs process vast amounts of data, sometimes including personal identifiable information (PII), financial data and confidential enterprise information. Without a robust safeguarding mechanism, sensitive data can inadvertently be retained by models or exposed through outputs. Key challenges include:

1. Identifying sensitive information in unstructured data like text, audio, and images
2. De-identifying data without losing its utility for model training
3. Maintaining compliance with complex regulations
4. Securing data shared between parties for collaborative AI projects

Solution

Together with Intel, Lanner aims to bring edge AI closer to real-time data analysis at the network edge and introduced the ECA-6051, a 2U 19” modular edge AI server that leverages Intel® Xeon® Processor Scalable Family (Codenamed Sierra Forest-SP/Granite Rapids-SP/Clearwater Forest-SP) for accelerating AI inference at the 5G edge.

This powerful platform is designed to enhance AI-driven workloads, ensuring rapid data processing and real-time insights while supporting high-bandwidth 5G connectivity, fulfilling the significantly increased demand for low-latency AI applications found in telecommunications, autonomous systems and smart cities, such as video transcoding, factory visual inspection and RAN intelligent control.

Together with a comprehensive privacy-preserving approach by way of a proprietary LLM privacy safeguarding software that isolates, protects and governs privacy rules, Lanner’s ECA-6051 can be relied upon for accomplishing the followings:

• Employing methods such as tokenization, masking, and differential privacy to protect sensitive data during training and inference. Sensitive text data for instance can be tokenized, ensuring that the original information is not stored in plain text while maintaining its format for effective training.
• Enabling fine-grained and customizable access control, not only ensuring that sensitive data is only accessible to authorized users but also mitigating risks during data sharing and model outputs.
• Enforcing integration and compliance, delivering seamless integration with existing AI infrastructures, therefore supporting compliance with global privacy standards.
• Managing data dictionary and sensitive data, affording businesses the ability to define sensitive terms and to make sure that they are excluded from training datasets or appropriately protected during inference.

Outcome

Building private LLM using tokenization and masking is made possible because Lanner’s hardware solutions, such as the ECA-6051 delivers computing performance that facilitates the handling of sensitive information, ensures data protection during training and makes certain that no sensitive data are leaked into models or outputs. The result is a privacy-compliant AI system that improves diagnostic accuracy, simplifies adherence to data residency and privacy regulations, all without compromising data privacy and information integrity so that one can focus on core AI development rather than data security.

Conclusion

When trying to build a private LLM, Lanner’s ECA-6051 is capable of delivering unparalleled computing power for AI inference, training, machine learning, memory-intensive applications and complex AI algorithms. Lanner’s ECA-6051 is 5G-ready, perfect for deployment in space-constrained 5G edge network locations and supports low-latency, high-bandwidth applications that are crucial for all industries that require real-time data insights.

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