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

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

Study on A Proposed Scheme for Generating Inverted Encryption Index Structure Based on Public Homomorphic Encryption

This research article focuses on the formidable challenge of efficiently searching through encrypted data in cloud environments, particularly as an extended number of users adopt encryption for their sensitive Information. The inverted index has proven to be a robust and effective searchable index structure in this context. However, striking a balance between preserving user privacy and enabling conjunctive multi-keyword searches remains a significant hurdle for existing solutions. In response to this challenge, the authors propose an innovative public-key-based encrypted file system. This system follows conjunctive multi-keyword searches but also eliminates the restrictive one-time-only searching limitation that has been a drawback in previous approaches. The proposed solution goes beyond conventional methods by safeguarding the search pattern, a critical aspect of user privacy. Their approach involves the integration of a probabilistic trapdoor- generating mechanism, adding an extra layer of security. To fortify their technique and adhere to more stringent security standards, the authors introduce an oblivious transmission control mechanism. This mechanism enhances the overall security posture of the system, ensuring robust protection against potential threats. The simulation results presented in the article demonstrate the practical proposed technique in real-world applications. Despite the additional security measures, the approach incurs reasonable overhead, making it a viable and efficient solution for cloud-based encrypted data searches

Survey Paper on Multi Keyword Similarity Search over Encrypted Cloud Data

The tremendous amount of data outsourced every day by individuals or each enterprises . It is impossible to manage or to store this complex data at individual level, as the chances of crash the system is more, and the system becomes the single point of failure.When we feel the need of storing the data in such a way that it can be accessed uninterruptedly, then there the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive. Considering the privacy of the data over the cloud, for that searchable encryption can be used. At the time of retrieval of data, consider the multi-keyword search over outsourced cloud text data only as it can handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Searchable encryption also enables searching without revealing any additional information. Using multi-keyword similarity search cloud returns the files containing more number of matches with user input keywords and similar keyworks. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Filter’s bit pattern is used to speedup and it is efficient in terms of the search time at the cloud side. This paper presents a review on various existing Similarity searching techniques