Big data, differential privacy and national statistical organisations

Article type: Research Article

Authors: Bailie, James

Affiliations: Department of Statistics, Harvard, One Oxford Street Cambridge, MA 02138-2901, USA | Tel.: +1 617 495 5496; E-mail: [email protected]

Correspondence: [*] Corresponding author: The Australian National University, ACT, Australia. Tel.: +61 4 0026 2577; E-mail: [email protected].

Abstract: Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual’s privacy resulting from the publication of a statistical output such as a frequency table. This paper provides an introduction to DP for official statisticians and discuss its relevance, benefits and challenges from a National Statistical Organisation (NSO) perspective. We motivate our study by examining how privacy is evolving in the era of big data and how this might prompt a shift from traditional statistical disclosure techniques used in official statistics – which are generally applied on a cell-by-cell or table-by-table basis – to formal privacy methods, like DP, which are applied from a perspective encompassing the totality of the outputs generated from a given dataset. We identify an important interplay between DP’s holistic privacy risk measure and the difficulty for NSOs in implementing DP, showing that DP’s major advantage is also DP’s major challenge. This paper provides new work addressing two key DP research areas for NSOs: DP’s application to survey data and its incorporation within the Five Safes framework.

Keywords: Data privacy, statistical disclosure control

DOI: 10.3233/SJI-200685

Journal: Statistical Journal of the IAOS, vol. 36, no. 4, pp. 1067-1074, 2020