Perturbation Methods for Protecting Data Privacy: A Review of Techniques and Applications

Perturbation methods are mathematical techniques used to add controlled noise or randomness data protect privacy while allowing analysis. Various methods, such as randomized response, differential privacy, secure multi-party computation, addition, and sampling aggregation, sensitive information from disclosure exploitation. These have been successfully applied in machine learning, statistics, cryptography ensure privacy. However, their implementation must be carefully designed avoid compromising accuracy introducing bias Mostly, perturbation offer a promising approach various fields. This review provides an overview of fields, including cryptography. involve adding preserve still