LSPSS: Constructing Lightweight and Secure Scheme for Private Data Storage and Sharing in Aerial Computing

IEEE Transactions on Services Computing(2023)

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Aerial computing is gradually playing an essential role in edge and fog computing paradigms by virtue of mobility, availability, scalability, flexibility, and simultaneity, where the Low-altitude Computing (LAC) platform, as the end close to the data sources, is mainly responsible for data collection and storage. However, because of the long physical distance of data transmission and the vulnerability of the transmission link to various attacks, how to efficiently share the stored data while ensuring data privacy is a critical issue for LAC at present. In this paper, we propose a lightweight and secure private data storage and sharing scheme to support range queries over encrypted multi-dimensional data. Specifically, we first propose two data conversion methods for transforming location features and collected log files with multi-dimensional attributes in Unmanned Aerial Vehicles (UAVs). Based on the ideas of asymmetric scalar-product-preserving encryption (ASPE) and inner product comparison (IPC), we design a privacy-preserving storage and sharing technique for the converted data. In addition, to achieve secure and efficient data querying and result verification, we design a secure data index and build a data authentication structure (DAS) with G-tree. Finally, we rigorously analyze the security of our proposed scheme and conduct extensive experiments on a real-world database to prove that our proposed scheme is secure and easy to use in practical application scenarios.
Aerial Computing,Unmanned Aerial Vehicle (UAV),Multi-dimensional Data,Privacy-preserving
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