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A CNN-based Column Prediction Model for Generating SQL Queries using Natural Language
Yoonki Jeong, Dongmin Kim, Jongwuk Lee
http://doi.org/10.5626/JOK.2019.46.2.202
To retrieve massive data using relational database management system (RDBMS), it is important to understanding of table schemas and SQL grammar. To address this issue, many studies have recently been carried out to generate an SQL query from a natural language question. However, the existing works suffer mostly from predicting columns at where clause and the accuracy is greatly reduced when there are multiple columns to be predicted. In this paper, we propose a convolutional neural network model with column attention mechanism that effectively extracts the latent representation of input question which helps column prediction of the model. The experiment shows that our model outperforms the accuracy of the existing model (SQLNet) by 6%.
Privacy-Preserving Self-Certified Public Auditing for Secure Cloud Storage
Mokryeon Baek, Dongmin Kim, Ik Rae Jeong
With a cloud storage service, data owners can easily access their outsourced data in cloud storage on different devices and at different locations, and can share their data with others. However, as the users no longer physically have possession of their outsourced data and the cloud still facing the existence of internal/external threats, the task of checking the data integrity is formidable. Over recent years, numerous schemes have been proposed to ensure data integrity in an untrusted cloud. However, the existing public auditing schemes use a third-party auditor(TPA) to execute high computation to check data integrity and may still face many security threats. In this paper, we first demonstrate that the scheme proposed by Zhang et al. is not secure against our two threat models, and then we propose a self-certified public auditing scheme to eliminate the security threats and guarantee a constant communication cost. Moreover, we prove the securities of our public auditing scheme under three security models.
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