Differential Privacy

Differential privacy is a mathematical framework that enables organizations to share data insights while protecting individual data privacy by adding noise to the data, making it difficult to identify specific individuals. As data-driven research and applications continue to grow, differential privacy has become increasingly relevant in the tech community, providing a rigorous and quantifiable approach to balancing data utility with individual privacy, and is being adopted in various fields, including machine learning, data analytics, and healthcare research.