Big data, differential privacy and national statistical organisations
Big data, differential privacy and national statistical organisations
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大数据、差异隐私和国家统计组织
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
J. Bailie
中科院分区：
文献类型：
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作者：
J. Bailie
差分隐私（DP）已经出现在计算机科学文献中，作为对个人隐私的影响的度量，这些隐私是由诸如频率表之类的统计输出的发布引起的。本文为官方统计人员介绍了发展伙伴关系，并从国家统计组织的角度讨论了其相关性、好处和挑战。我们通过研究隐私如何在大数据时代发展来激励我们的研究，以及这如何促使从官方统计中使用的传统统计披露技术（通常逐个单元或逐个表格应用）转变为正式的隐私方法，如DP，从涵盖给定数据集生成的全部输出的角度应用。我们确定DP的整体隐私风险措施和困难的国家统计局在实施DP之间的重要相互作用，DP的主要优势也是DP的主要挑战。本文提供了新的工作，解决两个关键的DP研究领域的国家统计局：DP的应用调查数据，并将其纳入五个安全框架。
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.
影响因子：
10.7
作者：
Abowd, John M.;Schmutte, Ian M.
通讯作者：
Schmutte, Ian M.