An Efficient Data Privacy Protection System Based on Differential Privacy

The daily services we use, such as search engines, mobile services, and online social activity, all have vast sensitive personal information in today's information realm. The question of how to aggregate sensitive user data without compromising individual privacy is a fundamental obstacle to increasing data accessibility. Differential privacy has become a widely accepted method for disclosing sensitive information while ensuring statistical privacy. There are numerous algorithms that can be used to address various target functions. However, we have identified the research gap in some research paper is that the noise added data used in the existing algorithm is in decimal values, and in some cases, it is negative as well. In general, count or frequency can neither be negative nor decimal, but should rather be a whole number. As a consequence, a good mechanism for differential privacy core count is required in order to achieve better and reliable data privacy. In this paper, we focus on developing a unique mechanism to protect the sensitive information with the use of differential privacy. Laplace noise addition is frequently advanced as a method for satisfying differential privacy. We curate a dataset by collecting user responses by a survey form and stored as an actual dataset to which the random noise is added to produce the noise added dataset using the differential privacy algorithm that is Laplace algorithm. The Randomized Response Algorithm is used to create the noise added dataset. Implementation of Laplace mechanism algorithm is implemented by C++ and Rust and the result is compared with the existing algorithms. Our algorithm only produces whole number that is integers as the count values and produced results with better accuracy than the existing Laplace algorithm.