GDPRePrivacy

GDPRePrivacy is a privacy-preserving framework for estimating and publishing gross domestic product (GDP) statistics. It combines privacy-enhancing technologies such as differential privacy, secure multi-party computation, and data governance to enable the release of aggregated national accounts data while protecting individual firm-level or location-level information. The concept aims to reconcile the public value of macroeconomic statistics with stringent data protection requirements across jurisdictions.

Principles include minimizing data collection, calibrating privacy budgets, and providing auditable trails. Data from multiple sources—such as tax records, business surveys, and administrative datasets—are brought together through secure computation or synthetic data generation, with noise added to protect disclosures. Output aggregates are designed to meet predefined accuracy targets and disclosure controls.

GDPRePrivacy is discussed as a tool for national statistical offices, central banks, and researchers seeking timely GDP indicators without compromising confidentiality. It supports cross-country comparisons through standardized privacy parameters and governance frameworks, and may enable more frequent releases while maintaining public trust.

Advocates argue it reduces disclosure risk, expands data-sharing opportunities, and enhances resilience against data breaches. Critics caution that added noise can distort estimates, complicate international comparability, and require substantial technical capacity and clear standards for validation and oversight.

While not a widely adopted standard, GDPRePrivacy has been the subject of policy papers and pilot projects exploring privacy-preserving macroeconomic statistics in recent years.