An Adaptive Multilevel Secure Searchable Encryption Scheme for Image Privacy Protection in Internet of Vehicles
Chunjiang Lai, Yuning Qi, Boyang Zhou, Jingguo Bi, Lixiang Li, Haipeng Peng, Xiaofei He · IEEE Internet of Things Journal · 2025
In the context of connected vehicles, encrypted image search technologies have gained significant importance. However, existing techniques are plagued by several limitations, including high resource consumption, lack of flexibility, insufficient security evaluation, and absence of hierarchical security mechanisms. These constraints render them inadequate for meeting the diverse resource requirements and multi-level security needs of different devices within the connected vehicle ecosystem. Therefore, improvements are urgently needed to enhance efficiency and security. In light of these issues, we introduce a novel framework designed for the secure retrieval of k-nearest Neighbor (kNN) images, leveraging cloud-based storage. This framework is underpinned by a Self-Adaptive Asymmetric Scalar Product Homomorphic Encryption algorithm (SA-ASPE). It incorporates an adaptive block mechanism to generate a suite of encryption keys tailored for images of diverse dimensions. Additionally, a security hierarchy, facilitated by a recursive split tree, is established to provide a selection of multi-tiered security options suitable for a variety of IoT contexts. To further augment the security of image feature vectors, we have integrated chaotic encryption techniques, which serve to thoroughly randomize these vectors, thereby significantly enhancing their unpredictability. To rigorously assess the robustness of the ASPE protocol against potential security threats, we have devised a comprehensive four-tier attack model coupled with an Amplified Attack (AA) strategy. Ultimately, through an extensive comparative analysis of existing and proposed schemes, we demonstrate that our framework adeptly satisfies the efficiency and security demands of a multitude of IoT device privacy protection scenarios.