Explores the technological shift from traditional k-anonymity to the more robust framework of differential privacy within the modern data economy.

It details how early methods of de-identification failed due to re-identification attacks, leading to the development of syntactic models that group similar records together.

The source then contrasts these methods with differential privacy, a mathematical approach that injects noise into computations to provide a provable guarantee of individual anonymity.

By analyzing the technical mechanisms of both systems, the text highlights the trade-offs between data utility and the rigor of protection against sophisticated attacks.

Finally, it examines real-world applications, such as the U.S. Census, to demonstrate how these privacy-enhancing technologies are implemented by major institutions.