Secure Cloud-Based Management of Health Care Big Data Using GANs and Ant Colony Optimization
Keywords:
Big Data, Cloud Computing, Data Privacy, Ant Colony Optimization (ACO), Generative Adversarial Networks (GANs), Healthcare Data, Data EncryptionAbstract
The exponential growth of digital information in the healthcare industry has led to the generation of vast amounts of data, known as Big Data. Traditional data storage systems are incapable of handling such large volumes of data, making it challenging to analyse using typical analytic tools. Cloud computing has emerged as a solution to address the challenges of managing, storing, and analysing Big Data by distributing large datasets over a network of cloudlets. However, storing private data in the cloud raises concerns about data leakage and lack of user control. This study introduces a system for secure data storage utilizing Ant Colony Optimization and Generative Adversarial Networks (GANs). The process begins with data normalization through Filter Splash Z normalization, followed by the application of GANs to assess similarity, thereby ensuring data accuracy and reducing computational expenses. A novel encryption approach is employed to safeguard outsourced data, preventing the exposure of sensitive information. The research utilized health data from a major city, sourced from the Kaggle database. The proposed encryption technique enables users to maintain privacy while efficiently storing vast amounts of data in the cloud, resulting in time and cost savings. This innovative framework has the potential to transform healthcare decision-making by offering data-driven insights while maintaining the highest standards of confidentiality and privacy protection.
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