Systematic review on privacy categorisation

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Paola Inverardi , Patrizio Migliarini , Massimiliano Palmiero
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引用次数: 0

Abstract

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people’s decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorisation, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorisation involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorisation has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorisation as a research attempt is still meaningful and may have a future.

私隐分类系统检讨
在现代数字世界中,用户需要做出具有深远影响的隐私和安全选择。研究人员越来越多地研究人们在面临隐私和安全权衡时的决策,影响这些决策的紧迫和耗时的抑制因素,以及缓解这些影响的方法。这项工作旨在对隐私分类的文献进行系统综述,隐私分类已从个人资料、特征分析、分割、聚类和人物角色等方面进行了定义。隐私分类包括根据特定的先决条件对用户进行分类的可能性,例如他们管理隐私问题的能力,或者根据他们决定或不决定披露的个人信息的类型和数量。隐私分类已被定义并用于不同的目的。系统综述侧重于调查研究背景的三个主要研究问题,即提出隐私分类的动机和研究问题;隐私分类的方法和结果;隐私分类随时间的演变。最终,它试图提供一个答案,即隐私分类作为一种研究尝试是否仍然有意义,是否有未来。
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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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