A secure data sharing model against keyword guessing attacks in edge-cloud collaboration scenarios

Abstract In the context of edge cloud collaboration scenarios, data sharing is an essential technological tool, and smart devices face significant challenges when it comes to data sharing security. In scenarios involving edge clouds, attribute-based keyword search (ABKS) is utilized to enable fine-grained access control over shared data and keyword retrieval by users who meet the required privileges. The implementation of secure data sharing is threatened since most of the current ABKS protocols are unable to fend against Keyword Guessing Attacks (KGA), which can be launched by an untrusted cloud server and result in the exposure of sensitive personal information. Using attribute-based encryption (ABE) as the foundation, we build a secure data exchange paradigm in this work that withstands the keyword guessing attack.In our paper, we provide a secure data sharing framework that resists keyword guessing attacks and uses attribute-based encryption (ABE) as the foundation to achieve fine-grained access control to resources in the ciphertext. To avoid malicious guessing of keywords by cloud servers, two encryption keys computed by edge layer key negotiation mechanism re-encrypt the user's private key and keyword trapdoor, respectively. The model is implemented using the JPBC library. According to the security analysis, the model is able to resist Keyword Guessing Attacks (KGA) in the random oracle model. The model's performance examination demonstrates its feasibility and lightweight nature, as well as its large computing advantages and lower storage consumption.