An enhanced sensitive information security model

Sensitive Information Security (SIS) model provides the strong base to transmit a large volume of sensitive information based textual documents securely and safely. In this paper, a model is proposed to provide sensitive information security using new techniques for dimensionality reduction where two new algorithms Term Similarity Clustering (TSC) and Term Index Clustering (TIC) based on supervised learning are proposed for SIS model. The TSC approach includes the lossy data reduction at the sender side, but makes the system tolerable to maintain integrity by keeping confidentiality at its best level when data is sent over on the unsecured communication channel. The terms or words are extracted from the text documents and they are categorized into Term Clusters (TC) with the use of Knowledge Repository (KR). Otherwise, it uses the KR and then makes a new entry into the appropriate TC with its associated TSC. Then these TCs of reduced dimension are provided the high security and sent over on the unsecured channel. The second approach TIC works differently and provides the lossless data reduction at the sender side with high data integrity and confidentiality during data transmission but includes an overhead of keeping KR at the receiver side. In TIC, instead of keeping the word in the TC, the index of each term, referring the knowledge repository, is placed in the respective TIC. Due to the KR overhead, this approach increases the configuration complexity at the receiver side. As both proposed approaches decrease the space and time complexities, so this paper provides the analytical experimental results of testing a text document of NASA Standards in which TSC gives about 10% space dimension reduction with some information loss, whereas TIC provides around 12% of space reduction with the additional overhead of KR at the receiver side.