Abstract
At present huge amount of data are moving through internet, this creates a huge concern for the security of the data while it is being moved through unsecured medium like wireless internet. Among all data most commonly used data format is text data which even though being highly vulnerable to security breaches is used in essential areas like banking and database management. There are many methodologies proposed to secure this data but the possibility of data being exploited remains. The proposed system presents a new symmetric encryption technique where complexities of calculating determinants and developing random matrices are used to generate keys. The system consists of three phases, key generation, encryption and decryption. In the first phase keys are generated which are computed by calculating the determinants of decomposed matrices, these decomposed matrices are generated from the multiplication of the original random matrices. In the second phase a key is randomly selected and is added with the ASCII value of the text message. The new generated value is then multiplied with the multiplication factor which is decomposition of selected key into the range of zero to nine. In the last phase the encrypted text is reverted back to its original text by performing reverse operations performed during encryption phase i.e. division of ASCII value of encrypted text by multiplication factor and then subtraction of the value of key from the computed result. The performance of the system is measured by calculating the total time taken to generate key with encryption and decryption of the input text message. The proposed system has been compared with various existing robust systems like AES, DES and BLOWFISH. And from the comparison it can be asserted that the proposed system is more efficient than the existing systems.
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Mante, P.G., Oswal, H.R., Swain, D., Deshpande, D. (2020). A Symmetrical Encryption Technique for Text Encryption Using Randomized Matrix Based Key Generation. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_13
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DOI: https://doi.org/10.1007/978-981-15-0978-0_13
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