A PRIVACY-PRESERVING FRAMEWORK FOR ETHICAL AND SECURE MACHINE LEARNING.

Abstract

In today's competitive, data-driven world, businesses heavily rely on customer data to obtain insights and maintain an advantage over their rivals. However, growing concerns about data security and privacy have created numerous barriers to using this data responsibly. Tokenization, end-to-end encryption, differential privacy, and federated learning are advanced privacy-preserving technologies that are secure and ethically sound when used in the machine learning architecture suggested in this work. By enabling businesses to analyse aggregated data while maintaining individual privacy, the framework ensures that customer insights are gained without endangering sensitive data. Differential privacy, which introduces noise to data points, further enhances privacy protection by making it more difficult to connect certain data to particular people.