Differential privacy enhances privacy in public sector data sharing, supporting the improvement of public services and policy making without compromising individual privacy. It enables data analysis, policymaking, and research while protecting individual confidentiality.
This approach, mathematically proven to safeguard privacy, boosts public trust, crucial in times of increasing data breaches and privacy concerns. With assured privacy protection, individuals are more likely to share data, improving public dataset quality and accuracy.