期刊
EPJ DATA SCIENCE
卷 10, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1140/epjds/s13688-020-00257-4
关键词
Sensitive data; Data visualizations; Disclosure control; Privacy protection; Anonymization
资金
- European Commission [824989]
- European Union's Horizon 2020 research and innovation programme [874583]
- UK Department of Health and Social Care [RES/0150/7943/202]
- Wellcome Trust [102215, 108439/Z/15/Z, MR/N01104X/1, MR/N01104X/2]
- Medical Research Council [108439/Z/15/Z, MR/N01104X/1, MR/N01104X/2]
- Economic and Social Research Council [MR/N01104X/1, MR/N01104X/2]
- Health Data Research UK [MR/S003959/1]
- National Institute for Health Research Applied Research Collaboration
- Public Health England
- European Union [786247]
- UK's Medical Research Council [MC_PC_17210]
- UKRI Innovation Fellowship
- Marie Curie Actions (MSCA) [786247] Funding Source: Marie Curie Actions (MSCA)
- MRC [MR/S003959/1] Funding Source: UKRI
- Wellcome Trust [108439/Z/15/Z] Funding Source: Wellcome Trust
Data visualizations are valuable tools that graphically reveal information about data structures, properties, and relationships between variables. However, in sensitive fields like medicine and social sciences, restrictions are placed on sharing individual-level records to protect privacy. Anonymization techniques such as k-anonymization and probabilistic perturbation can be used to generate privacy-preserving visualizations while adhering to data protection laws. These methods allow for exploration and inferential analysis while maintaining data confidentiality.
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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