Privacy Preserving Student Feedback System using Homomorphic Encryption
Shweta Arunakar Hegde, Nikshitha N. Shetty, Pavitra Nagesh Soma, Nikhil Nagaraj Naik, Ramachandra H. N, K. S. Shivaprakasha · 2024
In educational institutions, collecting and analyzing student feedback is crucial for enhancing teaching methodologies and improving overall educational experiences. However, the sensitive nature of student feedback demands a robust system that ensures both security and privacy. The proposed system leverages Homomorphic Encryption (HE) techniques to secure the confidentiality of student feedback data. By allowing computations on encrypted data without the need for decryption, the system enables meaningful analysis while preserving the privacy of individual feedback contributors. The encryption ensures that even in the event of unauthorized access, intercepted data remains unreadable without the appropriate decryption keys. This work offers a comprehensive solution, striking a crucial balance between data utility and privacy in educational feedback processes. It serves as a model for integrating advanced cryptographic techniques into educational systems, ensuring the security and privacy of sensitive information.