发表机构
Harvard University; The Australian National University; Australian Bureau of Statistics（哈佛大学; 澳大利亚国立大学; 澳大利亚统计局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对国家统计机构，介绍差分隐私，探讨其在大数据时代的相关性、优势与挑战，研究其在调查数据及五大安全框架中的应用。
AI 中文摘要
差分隐私（Differential Privacy，DP）在计算机科学文献中已成为衡量统计输出（如频率表发布）对个人隐私影响的一种度量标准。本文为官方统计人员介绍DP，并从国家统计机构（National Statistical Organisation，NSO）的角度探讨其相关性、优势与挑战。我们通过考察大数据时代隐私的演变情况，以及这可能如何促使官方统计中使用的传统统计披露技术（通常按单元格或表格应用）转向DP等正式隐私方法来为研究提供动机，DP是从涵盖给定数据集生成的全部输出的视角应用的。我们发现DP的整体隐私风险度量与NSO实施DP的困难之间存在重要相互作用，表明DP的主要优势也是其主要挑战。本文提供了针对NSO的两个关键DP研究领域的新工作：DP在调查数据中的应用及其在“五大安全”（Five Safes）框架内的整合。
英文摘要
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.
Comments15 pages plus references, 2 tables, 2 figures
Journal refStatistical Journal of the IAOS 36, no. 4 (2020): 1067-1074
