Galois Differential Privacy is a data privacy solution that enables organizations to share and analyze sensitive statistical data while protecting individual privacy. The technology introduces statistical noise to query responses, preventing the identification of specific individuals while maintaining the validity and usefulness of aggregated statistical results. The solution provides mathematical guarantees of privacy protection that remain effective even as more data and advanced de-anonymization techniques become available. It addresses limitations of traditional anonymization techniques that are vulnerable to data linking attacks. The technology is designed for organizations in healthcare, finance, government statistics, and telecommunications that need to extract statistical insights from sensitive data while maintaining regulatory compliance. It allows data access and analysis without compromising individual privacy, helping organizations avoid reputational damage and non-compliance penalties associated with data breaches. The differential privacy approach balances privacy protection with data utility by adding controlled amounts of statistical noise to datasets, ensuring that aggregate results remain statistically significant for decision-making, pattern recognition, and service personalization while preventing individual identification.