Privacy preserving data visualizations.
Privacy preserving data visualizations.
复制标题
隐私保护数据可视化。
DOI：
10.1140/epjds/s13688-020-00257-4
复制
发表时间：
2021
期刊：
影响因子：
3.6
通讯作者：
Burton P
中科院分区：
文献类型：
--
作者：
Avraam D;Wilson R;Butters O;Burton T;Nicolaides C;Jones E;Boyd A;Burton P
数据可视化是在统计分析和结果解释过程中使用的宝贵工具，因为它们以图形方式显示了关于变量之间的结构、属性和关系的有用信息，否则这些信息可能隐藏在表格数据中。在医学和社会科学等学科中，收集的数据包括有关研究参与者的敏感信息，个人记录的共享和发布受到数据保护法和伦理法律规范的控制。因此，由于数据可视化--如图表和曲线图--可能会链接到其他发布的信息，并用于识别研究参与者及其个人属性，因此它们的创建往往受到数据使用条款的禁止。强制执行这些限制是为了降低违反数据主题保密的风险，但它们限制了分析师为其研究特征和发现显示有用的描述性图表。在这里，我们建议使用匿名化技术来生成保护隐私的可视化，该可视化保留了底层数据的统计属性，同时仍然遵守严格的数据公开规则。我们演示了使用(I)众所周知的k-匿名化过程，它通过使用抑制和泛化来减少数据的粒度来保护隐私，(Ii)一种新的确定性方法，它用每个k个最近邻居的质心来代替个体水平的观测，以及(Iii)一种通过添加随机随机噪声来扰动个体属性的概率过程。我们应用所提出的方法来生成用于探索性数据分析和推论回归图诊断的隐私保护数据可视化，并讨论了它们的优点和局限性。
Data visualizations are a valuable tool used during both statistical analysis and the interpretation of results as they graphically reveal useful information about the structure, properties and relationships between variables, which may otherwise be concealed in tabulated data. In disciplines like medicine and the social sciences, where collected data include sensitive information about study participants, the sharing and publication of individual-level records is controlled by data protection laws and ethico-legal norms. Thus, as data visualizations – such as graphs and plots – may be linked to other released information and used to identify study participants and their personal attributes, their creation is often prohibited by the terms of data use. These restrictions are enforced to reduce the risk of breaching data subject confidentiality, however they limit analysts from displaying useful descriptive plots for their research features and findings. Here we propose the use of anonymization techniques to generate privacy-preserving visualizations that retain the statistical properties of the underlying data while still adhering to strict data disclosure rules. We demonstrate the use of (i) the well-known k-anonymization process which preserves privacy by reducing the granularity of the data using suppression and generalization, (ii) a novel deterministic approach that replaces individual-level observations with the centroids of each k nearest neighbours, and (iii) a probabilistic procedure that perturbs individual attributes with the addition of random stochastic noise. We apply the proposed methods to generate privacy-preserving data visualizations for exploratory data analysis and inferential regression plot diagnostics, and we discuss their strengths and limitations.
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DOI：
10.1007/s10742-016-0162-8
发表时间：
2016-12-01
影响因子：
1.5
作者：
Matthews, Gregory J.;Harel, Ofer;Aseltine, Robert H., Jr.
通讯作者：
Aseltine, Robert H., Jr.
影响因子：
0.9
作者：
Avraam, Demetris;Boyd, Andy;Burton, Paul
通讯作者：
Burton, Paul
影响因子：
3.7
作者：
Cox, LH
通讯作者：
Cox, LH
DOI：
10.1146/annurev-soc-071312-145551
发表时间：
2014-01-01
期刊：
ANNUAL REVIEW OF SOCIOLOGY, VOL 40
影响因子：
--
作者：
Healy, Kieran;Moody, James
通讯作者：
Moody, James
影响因子：
7.7
作者：
Gaye A;Marcon Y;Isaeva J;LaFlamme P;Turner A;Jones EM;Minion J;Boyd AW;Newby CJ;Nuotio ML;Wilson R;Butters O;Murtagh B;Demir I;Doiron D;Giepmans L;Wallace SE;Budin-Ljøsne I;Oliver Schmidt C;Boffetta P;Boniol M;Bota M;Carter KW;deKlerk N;Dibben C;Francis RW;Hiekkalinna T;Hveem K;Kvaløy K;Millar S;Perry IJ;Peters A;Phillips CM;Popham F;Raab G;Reischl E;Sheehan N;Waldenberger M;Perola M;van den Heuvel E;Macleod J;Knoppers BM;Stolk RP;Fortier I;Harris JR;Woffenbuttel BH;Murtagh MJ;Ferretti V;Burton PR
通讯作者：
Burton PR