An efficient snow flake schema with hash map using SHA-256 based on data masking for securing employee data

Abstract

In various organizations and enterprises, data masking is used to store sensitive data efficiently and securely. The data encryption and secret-sharing-based data deploying strategies secure privacy of subtle attributes but not secrecy. To solve this problem, the novel snowflake schema with the hash map using secure hash algorithm-256 (SHA-256) is proposed for the data masking. SHA-256 approach combines data masking by secret sharing for relational databases to secure both privacy as well as the confidentiality of secret employee data. The data masking approach supports preserving and protecting the privacy of sensitive and complex employee data. The data masking is developed on selected database fields to cover the sensitive data in the set of query outcomes. The proposed method embeds one or multiple secret attributes about multiple cover attributes in a similar relational database. The proposed method is validated through different performance metrics such as peak signal-to-noise ratio (PSNR) and error rate (ER) and it achieves the values of 50.084dB and 0.0281 when compared to the existing methods like Huffman-based lossless image coding and quad-tree partitioning and integer wavelet transform (IWT).