Privacy-Preserving Online Feedback System

In online feedback evaluation, data is immediately available for analysis and reporting. But response to online feedback evaluation is less due to lack of privacy. Solution to achieve adequate response rate is to improve privacy. This paper introduces a novel framework to a problem of privacy in online feedback system. Our focus is to evaluate and analyse feedback, without disclosing the actual data from user. Here, data has been randomized to preserve privacy of individual user. We study how to analyse private feedback without disclosing it to other user or any other party. To tackle this demanding problem, we develop a secure protocol to conduct the desired computation. We define a protocol using homomorphic encryption techniques to send the feedback while keeping it private. Finally, we present privacy and correctness analysis that validates the algorithm.