Enhancing IoT Security and Privacy with Trusted Execution Environments and Machine Learning

2023/05/04 by Peterson Yuhala, Yuhala, Peterson
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #Digital and Cyber Forensics #FOS: Computer and information sciences #Security and Verification in Computing

paper · pdf · doi:10.48550/arxiv.2305.02584

openalex publication_date 2023/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With the increasing popularity of Internet of Things (IoT) devices, security concerns have become a major challenge: confidential information is constantly being transmitted (sometimes inadvertently) from user devices to untrusted cloud services. This work proposes a design to enhance security and privacy in IoT based systems by isolating hardware peripheral drivers in a trusted execution environment (TEE), and leveraging secure machine learning classification techniques to filter out sensitive data, e.g., speech, images, etc. from the associated peripheral devices before it makes its way to an untrusted party in the cloud.

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