Output and Input Data Perturbations for Differentially Private Databases

In today's ultra-connected world, the production and consumption of digital data has become immensely huge in volume. Differential privacy is a relatively new approach which attempts to provide strong privacy protection to users' data, while still maintaining outside access to data. However, the comparison of these methods in terms of performance and database suitability remains an open question. In this paper, we will provide a comprehensive comparison of input and output data perturbations for differentially private databases and evaluate the results in terms of accuracy, privacy, efficiency and scalability.