In the digital era, the interaction between humans and technology is becoming more massive due to its efficient and effective ways to meet the work requirements. Human-Technology Interaction (HTI) is an intensive interaction between humans and technology to achieve organizational goals.
Results show that proposed technique is suitable for healthcare applications where both privacy protection and accurate prediction are necessary conditions. In order to validate the results, proposed method is compared with the existing technology on the basis of various evaluation parameters. Even after perturbation, technique preserves required significance of prediction results by maintaining the balance between utility and security of data. Then, random forest classifier is applied on both original and perturbed dataset to predict the disease. To accomplish this goal, a metaheuristic Firefly Optimization Algorithm (FOA) is deployed for random noise generation (instead of fixed noise) and this noise is added to the least significant bits of sensitive data. Focus of the work is to predict the disease of patients’ while preventing the privacy breach of their sensitive information.
Paper proposes an improved Privacy-Preserving Data Classification System for Chronic Kidney Disease dataset. Sensitive information, if not protected, can become the basis for linkage attacks. However, there is a negative impact like unauthorized access, misuse, stealing of the data, which violates the privacy concern of patients. With the continuous advancements in Information and Communication Technology, healthcare data is stored in the electronic forms and accessed remotely according to the requirements.
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