Enhancing Financial Data Security in Big Data Environments Using AES-Blowfish and Cloud-Aided Encryption Techniques

Big data analyzes various variables, such as conventional financial data, consumer behavior and sentiment from social media, to enable predictive modeling. Financial companies may now forecast future performance and recognize dangers posed by outside variables like political unrest. Another important advantage is real-time risk assessment, which enables ongoing observation and flexible modifications. The field of financial risk management is seeing a profound transformation due to the transformative influence of big data analytics. Ensuring secure communication channels among financial institutions and clients is crucial in the Fintech industry to safeguard confidential data, including private financial data, contact details and credentials. Encryption is essential to keep the sensitive and private information shared across these channels private. This research aims to protect intellectual property and private information due to the dynamic nature of the Internet. The major part of this study is to determine and evaluate potential solutions for data protection at each stage of the data life cycle, from data collecting to data processing. The report highlights the necessity for strong security procedures even when handling massive data volumes while acknowledging the potential of big data. This study proposes a method that combines the MapReduce Big data procedure with the encryption and decryption algorithms for AES and blowfish to improve big data with cloud-aided financial data protection. The first step of data protection was carried out using AES-Blowfish, and then the significant data architecture was implemented to improve the data security in cloud environments. Performance tests are conducted for the following criteria: throughput, compressed ratio, encryption time, decryption time and information loss, and the suggested strategy for these procedures is implemented using R programming.