Sun Xiaoling, Li Shanshan
School of Information Engineering, Institute of Disaster Prevention, Langfang, Hebei, China.
PeerJ Comput Sci. 2023 Jun 20;9:e1433. doi: 10.7717/peerj-cs.1433. eCollection 2023.
Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.
目前，目标检测的重点正转向整合从多个传感器获取的信息。面对来自各种传感器的海量数据，在云环境中传输和存储期间确保数据安全成为首要关注点。数据文件可以加密并存储在云中。在使用数据时，可以通过密文检索返回所需的数据文件，进而开发可搜索加密技术。然而，现有的可搜索加密算法主要忽略了云计算环境中的数据爆炸问题。云计算环境下的授权访问问题尚未得到统一解决，导致数据用户在处理越来越多的数据时浪费计算能力。此外，为了节省计算资源，加密云存储（ECS）可能仅返回搜索查询结果的一个片段，缺乏实用且通用的验证机制。因此，本文提出了一种针对云边缘计算环境量身定制的轻量级、细粒度可搜索加密方案。我们基于双线性对为终端设备生成密文和搜索陷门，并引入访问策略来限制密文搜索权限，这提高了密文生成和检索的效率。该方案允许在辅助终端设备上进行加密和陷门计算生成，而在边缘设备上进行复杂计算。所得到的方法确保了数据的安全访问、在多传感器网络跟踪中的快速搜索，并在保持数据安全的同时加快了计算速度。最终，实验比较和分析表明，所提出的方法将数据检索效率提高了约62%，将公钥、密文索引和可验证可搜索密文的存储开销减少了一半，并有效减轻了数据传输和计算过程中的延迟。
