Speaker
Description
Establishing standardized Statistical Disclosure Control (SDC) processes is vital as data-sharing demands increase, anonymization techniques advance, and principles for privacy preservation continue to develop. In response, we present a SDC Architecture to systematically plan, implement, and document SDC of microdata with the objective to improve consistency, transparency, and adaptability in SDC as well as to facilitate institutional learning and compliance.
The SDC Architecture comprises four core layers, reflecting the progression from data acquisition - disclosure control - dissemination - long-term stewardship. Each layer structures decision-making and documentation at a critical stage of the SDC process, addressing diverse implementation contexts. Available in both concise and comprehensive formats, the SDC Architecture addresses methodological, legal, institutional, and technical prerequisites, while accommodating diverse data domains and contextual requirements.
Fully aligned with statistical standards, the SDC Architecture integrates into broader data governance ecosystems, informing both internal workflows and external communication. By serving as a procedural guide and a documentation standard, the SDC Architecture supports reproducibility and comparability, fostering consistent, auditable, and high-quality anonymization practices across evolving legal, technical, and organizational landscapes.