Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems

TL;DR

This work addresses the cross-border data security and privacy challenges of large language models and IoT systems by introducing JAPD, a Jurisdiction-Aware, Privacy-by-Design architecture that unifies localized encryption, inference-time differential privacy with jurisdiction-specific budgets, and real-time compliance proofs. Through a multi-jurisdiction simulation, it demonstrates substantial security gains (unauthorized exposure under $5\%$, zero compliance violations) while preserving high utility ($>90\%$) and manageable overhead ($<20\%$). The approach counteracts inference-time memorization risks and cross-border data leakage, outperforming static encryption, federated learning, and simple data localization baselines. The methodology provides a practical blueprint for policy-aligned, globally deployable AI systems with strong, auditable privacy guarantees.

Abstract

The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and privacy challenges. When data traverses borders, it becomes subject to conflicting legal regimes, such as the EU's General Data Protection Regulation and China's Personal Information Protection Law, compounded by technical vulnerabilities like model memorization. Current static encryption and data localization methods are fragmented and reactive, failing to provide adequate, policy-aligned safeguards. This research proposes a Jurisdiction-Aware, Privacy-by-Design architecture that dynamically integrates localized encryption, adaptive differential privacy, and real-time compliance assertion via cryptographic proofs. Empirical validation in a multi-jurisdictional simulation demonstrates this architecture reduced unauthorized data exposure to below five percent and achieved zero compliance violations. These security gains were realized while maintaining model utility retention above ninety percent and limiting computational overhead. This establishes that proactive, integrated controls are feasible for secure and globally compliant AI deployment.