Evaluating privacy of individuals in medical data

被引:0
作者
Kroes, Shannon K. S. [1 ,2 ,3 ]
Janssen, Mart P. [1 ]
Groenwold, Rolf Hh [3 ]
van Leeuwen, Matthijs [2 ]
机构
[1] Sanquin Res, Amsterdam, Netherlands
[2] Leiden Univ, Leiden, Netherlands
[3] Leiden Univ, Med Ctr, Leiden, Netherlands
关键词
anonymization; data exchange; generalization; privacy; uniqueness; MICRODATA;
D O I
10.1771/1460458220983398
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 26 条
  • [1] [Anonymous], P 2006 ACM SIGMOD IN, DOI DOI 10.1145/1142473.1142500
  • [2] [Anonymous], 12 ACM SIGKDD INT C, DOI DOI 10.1145/1150402.1150499
  • [3] Brickell Justin, 2008, P 14 ACM SIGKDD INT, P70, DOI [DOI 10.1145/1401890.1401904, 10.1145/1401890.1401904]
  • [4] Publishing Microdata with a Robust Privacy Guarantee
    Cao, Jianneng
    Karras, Panagiotis
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2012, 5 (11): : 1388 - 1399
  • [5] Uniqueness of medical data mining
    Cios, KJ
    Moore, GW
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2002, 26 (1-2) : 1 - 24
  • [6] Dankar F. K., 2010, P 2010 EDBTICDT WORK, P1
  • [7] Dua D., UCI Machine Learning Repository
  • [8] A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
    El Emam, Khaled
    Dankar, Fida Kamal
    Issa, Romeo
    Jonker, Elizabeth
    Amyot, Daniel
    Cogo, Elise
    Corriveau, Jean-Pierre
    Walker, Mark
    Chowdhury, Sadrul
    Vaillancourt, Regis
    Roffey, Tyson
    Bottomley, Jim
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2009, 16 (05) : 670 - 682
  • [9] Transfer Learning with Partial Observability Applied to Cervical Cancer Screening
    Fernandes, Kelwin
    Cardoso, Jaime S.
    Fernandes, Jessica
    [J]. PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017), 2017, 10255 : 243 - 250
  • [10] Franconi L, 2004, ANN NY ACAD SCI, V3050, P262