EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing

Sensors

Kuan Zhang¹  Yintang Yang²  Kai Fan³  Jie Yin³  Hui Li³ 

[1] Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, NE, 68588 USA;Key Laboratory of the Ministry of Education for Wide Band-Gap Semiconductor Materials and Devices, Xidian University, Xi’an 710071, China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China;

关键词: edge computing;    privacy;    multi-keyword;    automatic error correction;    R-tree;    relevance ranked;   

DOI  :  10.3390/s18113616

来源: DOAJ

【 摘 要 】

Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing tasks from the original cloud computing model to the edge device, the message is running on computing resources close to the data source. The edge computing model can effectively reduce the pressure on the cloud computing center and lower the network bandwidth consumption. However, the security and privacy issues in edge computing are worth noting. In this paper, we propose an efficient auto-correction retrieval scheme for data management in edge computing, named EARS-DM. With automatic error correction for the query keywords instead of similar words extension, EARS-DM can tolerate spelling mistakes and reduce the complexity of index storage space. By the combination of TF-IDF value of keywords and the syntactic weight of query keywords, keywords who are more important will obtain higher relevance scores. We construct an R-tree index building with the encrypted keywords and the children nodes of which are the encrypted identifier FID and Bloom filter BF of files who contain this keyword. The secure index will be uploaded to the edge computing and the search phrase will be performed by the edge computing which is close to the data source. Then EDs sort the matching encrypted file identifier FID by relevance scores and upload them to the cloud server (CS). Performance analysis with actual data indicated that our scheme is efficient and accurate.

【 授权许可】

Unknown