dorsal/arxiv
View SchemaCross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems
| Authors | Chalitha Handapangoda |
|---|---|
| Categories | |
| ArXiv ID | 2601.06612vv1 |
| URL | https://arxiv.org/abs/2601.06612 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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"date_created": "2026-02-17T05:53:08.444000Z",
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"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\u0027s General Data Protection Regulation and China\u0027s 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.",
"arxiv_id": "2601.06612",
"authors": [
"Chalitha Handapangoda"
],
"categories": [
"cs.CR",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems",
"url": "https://arxiv.org/abs/2601.06612",
"version": "v1"
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