Work

SelTZ: Fine-Grained Data Protection for Edge Neural Networks Using Selective TrustZone Execution

Year: 2024

Type: article

Abstract: This paper presents an approach to protecting deep neural network privacy on edge devices using ARM TrustZone. We propose a selective layer protection technique that balances performance and privacy. Rather than executing entire layers within the TrustZone secure environment, which leads to significant performance and memory overhead, we selectively protect only the most sensitive subset of data from each layer. Our method strategically partitions layer computations between normal and secure worlds, optimizing TrustZone usage while providing robust defenses against privacy attacks. Through extensive experiments on standard datasets (CIFAR-100 and ImageNet-Tiny), we demonstrate that our approach reduces membership inference attack (MIA) success rates from over 90% to near random guess (50%) while achieving up to 7.3× speedup and 71% memory reduction compared to state-of-the-art approaches. On resource-constrained edge devices with limited secure memory, our selective approach enables protection of significantly more layers than full layer protection methods while maintaining strong privacy guarantees through efficient data partitioning and parallel processing across security boundaries.

Source: Electronics

DOI: 10.3390/electronics14010123

Authors: Sehyeon Jeong, Hyunyoung Oh

Institution: Gachon University

Language: English

Topic: Security and Verification in Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion