Puda: Private User Dataset Agent for User-Sovereign and Privacy-Preserving Personalized AI

• URL: http://arxiv.org/abs/2602.08268v2
• Date: Tue, 10 Feb 2026 05:00:53 GMT
• Title: Puda: Private User Dataset Agent for User-Sovereign and Privacy-Preserving Personalized AI
• Authors: Akinori Maeda, Yuto Sekiya, Sota Sugimura, Tomoya Asai, Yu Tsuda, Kohei Ikeda, Hiroshi Fujii, Kohei Watanabe,
• Abstract summary: We propose Puda, a user-sovereign architecture that aggregates data across services and enables client-side management.<n>Puda allows users to control data sharing at three privacy levels: (i) Detailed Browsing History, (ii) Extracted Keywords, and (iii) Predefined Category Subsets.<n>Our results show that Puda enables effective multi-granularity management, offering practical choices to mitigate the privacy-personalization trade-off.
• Score: 0.35132824436572685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personal data centralization among dominant platform providers including search engines, social networking services, and e-commerce has created siloed ecosystems that restrict user sovereignty, thereby impeding data use across services. Meanwhile, the rapid proliferation of Large Language Model (LLM)-based agents has intensified demand for highly personalized services that require the dynamic provision of diverse personal data. This presents a significant challenge: balancing the utilization of such data with privacy protection. To address this challenge, we propose Puda (Private User Dataset Agent), a user-sovereign architecture that aggregates data across services and enables client-side management. Puda allows users to control data sharing at three privacy levels: (i) Detailed Browsing History, (ii) Extracted Keywords, and (iii) Predefined Category Subsets. We implemented Puda as a browser-based system that serves as a common platform across diverse services and evaluated it through a personalized travel planning task. Our results show that providing Predefined Category Subsets achieves 97.2% of the personalization performance (evaluated via an LLM-as-a-Judge framework across three criteria) obtained when sharing Detailed Browsing History. These findings demonstrate that Puda enables effective multi-granularity management, offering practical choices to mitigate the privacy-personalization trade-off. Overall, Puda provides an AI-native foundation for user sovereignty, empowering users to safely leverage the full potential of personalized AI.

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