Preserving Privacy and Utility in LLM-Based Product Recommendations

• URL: http://arxiv.org/abs/2505.00951v1
• Date: Fri, 02 May 2025 01:54:08 GMT
• Title: Preserving Privacy and Utility in LLM-Based Product Recommendations
• Authors: Tina Khezresmaeilzadeh, Jiang Zhang, Dimitrios Andreadis, Konstantinos Psounis,
• Abstract summary: Large Language Model (LLM)-based recommendation systems leverage powerful language models to generate personalized suggestions.<n>This raises privacy concerns as user data is transmitted to remote servers, increasing the risk of exposure and reducing control over personal information.<n>We propose a hybrid privacy-preserving recommendation framework which separates sensitive from nonsensitive data and only shares the latter with the cloud.
• Score: 4.28766264863679
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM)-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems that rely on structured data and collaborative filtering, LLM-based models process textual and contextual information, often using cloud-based infrastructure. This raises privacy concerns, as user data is transmitted to remote servers, increasing the risk of exposure and reducing control over personal information. To address this, we propose a hybrid privacy-preserving recommendation framework which separates sensitive from nonsensitive data and only shares the latter with the cloud to harness LLM-powered recommendations. To restore lost recommendations related to obfuscated sensitive data, we design a de-obfuscation module that reconstructs sensitive recommendations locally. Experiments on real-world e-commerce datasets show that our framework achieves almost the same recommendation utility with a system which shares all data with an LLM, while preserving privacy to a large extend. Compared to obfuscation-only techniques, our approach improves HR@10 scores and category distribution alignment, offering a better balance between privacy and recommendation quality. Furthermore, our method runs efficiently on consumer-grade hardware, making privacy-aware LLM-based recommendation systems practical for real-world use.

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