Securing Personalization in LLMs: Privacy-Centric Strategies for Scalable AI

As large language models (LLMs) power increasingly personalized AI experiences, ensuring user data privacy becomes both a technological and ethical imperative. This session delves into cutting-edge approaches to privacy-preserving personalization—focusing on h organizations can deliver adaptive, user-centric experiences without compromising sensitive data.

We explore advanced frameworks that integrate technologies such as federated learning, voice biometrics, and differential privacy, enabling secure personalization at scale. Federated learning emerges as a key enabler, allowing decentralized training across millions of edge devices while preserving data ownership. Real-world architectures and deployment strategies will be discussed to highlight its practical scalability.

The session also examines secure computation environments, including Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs), to protect data during inference and model adaptation. These tools guard against common threats like side-channel attacks and unauthorized data access.

Further, we analyze the role of voice biometric authentication for continuous, passive verification—boosting both security and user convenience. Differential privacy techniques are unpacked to demonstrate how they safeguard user data by introducing calibrated noise, ensuring statistical privacy without degrading personalization quality.

Finally, we introduce adaptive user profiling mechanisms, such as hierarchical user models and context-aware profile switching, that enhance personalization while reinforcing privacy boundaries.

Attendees will leave with actionable strategies and architectural insights to build AI systems that are not only intelligent and responsive—but fundamentally trustworthy and private by design.