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With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process.

Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.