Privacy-Preserving User Memory Modeling for Personalized AI Assistants
Keywords:
user memory modeling; privacy; personalization; differential privacy; federated learning; large language models; AI governanceAbstract
Personalized AI assistants increasingly depend on user memory models to maintain preferences, interaction histories, and contextual knowledge across sessions. While such memory enables more coherent and efficient assistance, it also concentrates sensitive behavioral, cognitive, and social information in ways that raise serious privacy risks. This paper examines privacy-preserving user memory modeling from a system-level perspective, focusing on structural trade-offs rather than algorithm-level formalism. It begins by conceptualizing user memory as a dynamic sociotechnical construct shaped by contextual norms and regulatory constraints. The discussion then turns to architectural strategies including local-first design, differential privacy, federated learning, on-device inference, and retrieval-augmented memory. The paper further analyzes tensions among personalization quality, privacy guarantees, latency, robustness, fairness, and environmental sustainability. It considers deployment constraints across consumer devices and regulated domains such as healthcare. Governance mechanisms, including data protection law and AI regulation, are discussed alongside auditability and accountability requirements. The paper argues that privacy-preserving memory modeling should be treated as an infrastructural challenge requiring layered governance, modular architecture, and lifecycle-aware data management. It concludes with future directions for sustainable, user-controllable memory systems that balance personalization with long-term trust.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
