Big data, differential privacy, and national statistical organisations

The provided research introduces differential privacy (DP) as a formal method for quantifying privacy impact in statistical outputs, contrasting it with traditional statistical disclosure techniques. It evaluates DP's relevance, benefits, and challenges from the perspective of National Statistical Organisations (NSOs) amidst the evolving landscape of big data and privacy concerns. The paper suggests that the big data era necessitates a shift towards formal privacy methods, such as DP, which offer a more comprehensive approach to protecting individual privacy.

Why it matters

The adoption of formal privacy methods, such as differential privacy, represents a critical strategic imperative for organizations that handle sensitive data and produce statistical outputs. It addresses the growing need to protect individual privacy in the age of big data, potentially enhancing public trust and ensuring compliance with future data protection standards while enabling continued data utility.

Key insights

• Differential privacy (DP) quantifies the impact on an individual's privacy when statistical outputs are published, like frequency tables.
• The paper introduces DP to official statisticians, discussing its relevance to National Statistical Organisations (NSOs).
• It examines the benefits and challenges of DP within an NSO context.
• The study is motivated by the evolving nature of privacy in the big data era.
• There is a potential shift from traditional statistical disclosure techniques, which are cell-by-cell or table-by-table, to formal privacy methods like DP, which encompass the total dataset.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.02495