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FeaShare: Feature Sharing for Computation Correctness in Edge Preprocessing
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AI
DOI：10.1109/TMC.2024.3391294.png)
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Abstract
En 中文
Edge preprocessing is a critical service type in edge computing. However, untrusted edges may be malicious to provide incorrect computational results (i.e., edge tampering). Although some studies have considered the correctness of results, they have limitations when applied to edge preprocessing. We present FeaShare, a feature-sharing approach, to verify edge results. The process is integrated into normal service operations. Meanwhile, to overcome feature-based limitations, terminals obtain partial edge results for a set of data by executing a small number of computations. These partial results are leveraged to construct shared features, facilitating the detection of edge tampering even when the tampered portion is not directly related to the features. Subsequently, the shared features are mapped to pseudo-data and added to the terminal's data sequence, preventing features from influencing the results of terminal data. To resist edge attacks, both feature construction and placement are time-dependent and dynamic. FeaShare is not confined to specific edge tasks. We evaluate FeaShare using 3 typical scenes encompassing 5 applications. For instance, the evaluation utilizing the VGG model and CIFAR-10 dataset demonstrates a detection rate of 97%. Terminals perform approximately 10% of the edge's computation operations, and its overhead growth rate is less than 10%.
Keywords:
Image edge detection
Feature extraction
Computational modeling
Cryptography
Data models
Mobile computing
Complexity theory
Computation correctness
edge preprocessing
edge tampering detection
feature sharing
untrusted edge
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