Abstract
The reliability of any identification systems depends on the level of security provided to the storage of sensitive data. These identification systems provide information about people working in any organization which is intended to help the organization keep track and monitor their activity. Databases of such systems need to be fast, reliable and highly secured. Apart from providing authorization layer to the databases the data itself can be encoded and stored in a simple as well as easy form such that the data itself is secure and does not go through lossy transformation. In this paper, the data is encoded with the help of DAE (Deep Autoencoder) models and these encoded data are then merged with the images of respective people using RDH (Reversible Data Hiding) which is then stored as a simple image data. The data is then retrieved when required and decoded using the same autoencoder mode and checked for loss in the data. This helps us to easily store and send the data as a simple image and improves the security of the system. The models perform good on all datasets with simple autoencoders gives the best result with a loss of only 1.5% loss in data as compared to 3% and 7% by deep autoencoder and convolutional autoencoder respectively Keywords: Identification system, authorization, security, Deep Autoencoder, Reversible Data hiding, storage, transformation.
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Agarwal, D., Nayyar, A., Nagrath, P. (2022). Securing of Identification System Data Transmission Using Deep Autoencoders and Data Hiding. In: Luhach, A.K., Jat, D.S., Hawari, K.B.G., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2021. Communications in Computer and Information Science, vol 1575. Springer, Cham. https://doi.org/10.1007/978-3-031-09469-9_18
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