Engineering Privacy for Big Data Apps with the Unified Modeling Language

被引：14

作者：

Jutla, Dawn N. ^{[1
]}

Bodorik, Peter ^{[2
]}

Ali, Sohail ^{[1
,2
]}

机构：

[1] St Marys Univ, Sobey Sch Business, Halifax, NS B3H 3C3, Canada

[2] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 3J5, Canada

来源：

2013 IEEE INTERNATIONAL CONGRESS ON BIG DATA | 2013年

关键词：

Big data applications; Privacy by Design; PbD; privacy engineering; UML extensions; privacy services; requirements analysis; software engineering; anonymization; pseudonymization; use case diagrams; ARCHITECTURE;

D O I：

10.1109/BigData.Congress.2013.15

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes proposed privacy extensions to UML to help software engineers to quickly visualize privacy requirements, and design privacy into big data applications. To adhere to legal requirements and/or best practices, big data applications will need to apply Privacy by Design principles and use privacy services, such as, and not limited to, anonymization, pseudonymization, security, notice on usage, and consent for usage. We extend UML with ribbon icons representing needed big data privacy services. We further illustrate how privacy services can be usefully embedded in use case diagrams using containers. These extensions to UML help software engineers to visually and quickly model privacy requirements in the analysis phase; this phase is the longest in any software development effort. As proof of concept, a prototype based on our privacy extensions to Microsoft Visio's UML is created and the utility of our UML privacy extensions to the Use Case Diagram artifact is illustrated employing an IBM Watson-like commercial use case on big data in a health sector application.

引用

页码：38 / 45

页数：8

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