@article{Goyal_2022, title={An Efficient Method to Enhance Health Care Big Data Security in Cloud Computing Using the Combination of Euclidean Neural Network And K-Medoids Based Twin Fish Cipher Cryptographic Algorithm}, volume={45}, url={https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1978}, abstractNote={<p>Big data is a phrase that refers to the large volumes of digital data that are being generated as a consequence of technology improvements in the health care industry, e-commerce, and research, among other fields. It is impossible to analyze Big Data using typical analytic tools since traditional data storage systems do not have the capacity to deal with such a large volume of data. Cloud computing has made it more easier for people to store and process data remotely in recent years. By distributing large data sets over a network of cloudlets, cloud computing can address the challenges of managing, storing, and analyzing this new breed of data It’s possible for private data to be leaked when it is kept in the cloud, as users have no control over it. This paper proposes a framework for a secure data storage by using the K-medoids-based twin fish cipher cryptographic algorithm. We first normalize the data using the Filter splash Z normalization and then apply the Euclidean neural network to compute similarity, which ensures data correctness and reduces computational cost. As a result, the suggested encryption strategy is used to encrypt and decode the outsourced data, thereby protecting private information from being exposed. The whole experiment was conducted using health data from a large metropolis from the Kaggle database. Using the recommended encryption method, users will be able to maintain their privacy while saving time and money by storing their large amounts of data on the cloud.</p>}, number={1}, journal={International Journal of Computer (IJC)}, author={Goyal, Arnav}, year={2022}, month={Oct.}, pages={28–66} }