Summary of Privacy-preserving Customer Support: a Framework For Secure and Scalable Interactions, by Anant Prakash Awasthi et al.
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions
by Anant Prakash Awasthi, Girdhar Gopal Agarwal, Chandraketu Singh, Rakshit Varma, Sanchit Sharma
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Applications (stat.AP); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The PP-ZSL framework introduces a novel approach toPrivacy-Preserving Zero-Shot Learning (PP-ZSL) for large language models (LLMs). Unlike traditional ML methods, PP-ZSL eliminates the need for local training on sensitive data by leveraging pre-trained LLMs. The framework incorporates real-time data anonymization, retrieval-augmented generation (RAG), and robust post-processing to ensure compliance with regulatory standards. This combination reduces privacy risks, simplifies compliance, and enhances scalability and operational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps customer support be more efficient and user-friendly. But using machine learning can raise concerns about keeping people’s personal information private. Right now, there are ways to protect this data, but they have limitations. A new approach called PP-ZSL uses special language models to generate responses without needing sensitive data. This makes it safer for people and easier to follow rules like GDPR and CCPA. By analyzing real-time data and using techniques like RAG, PP-ZSL shows that AI can be both helpful and private. |
Keywords
» Artificial intelligence » Machine learning » Rag » Retrieval augmented generation » Zero shot
