Approach of a Multilevel Secret Sharing Scheme for Extracted Text Data
Shamal Kashid, Krishan Kumar, Parul Saini, Alok Negi, Ashray Saini · 2022
Text data pre-processing has become essential in research fields like Information Retrieval (IR), Natural Language Processing (NLP), and text mining. It extracts valuable and nontrivial information from unstructured text data. Text data plays an essential role in the digital era as various types of information and data are being generated in an unstructured way. In this work, Multi-level Secret Sharing Scheme (MSSS) based novel approach has been proposed to ensure the security of the most informative and sensitive text data extracted through text preprocessing steps from the text document. The proposed model employs tokenization, stop word removal, and stemming as preprocessing techniques, which help examine text documents. Three types (electronic mail, whats-app messages, and text messages) of datasets have been contributed to this work for performing the experiments, where the model achieves 100% correlation between original and reconstructed text.