Privacy Amplification: Harnessing Information Theory for Differential Privacy

Abstract

Privacy amplification, a concept rooted in information theory, serves as a pivotal mechanism for bolstering the guarantees of differential privacy. By strategically manipulating data through noise addition or perturbation, privacy amplification techniques aim to obscure sensitive information while preserving the integrity of statistical analyses. Leveraging mathematical frameworks such as the Rényi differential privacy and the concentrated differential privacy, these methods enable the optimization of privacy guarantees tailored to specific use cases. Through the careful calibration of noise parameters and data transformations, abstract privacy amplification provides a robust defense against privacy threats, ensuring that individual privacy remains intact even amidst the scrutiny of data-driven analyses and algorithms. This amalgamation of information theory principles with differential privacy mechanisms stands at the forefront of safeguarding privacy in an increasingly data-centric landscape.