A Multilateral Privacy Impact Analysis Method for Android Applications

Kelly E. Orjiude, C. Yinka-banjo
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引用次数: 1

Abstract

Abstract Most people’s private lives can be monitored by smartphone applications (apps). Apps have the potential to invade private spaces, access and map social interactions, track users’ whereabouts, and track their online activities. Our interest is in the volume of data that a specific app can and seeks to retrieve on a smartphone. Smartphone app privacy friendliness is normally evaluated based on single-source analyses, which often do not offer a thorough assessment of the app’s actual privacy threats. In order to analyze Android apps’ privacy, this study proposes a multi-source methodology. Our data sets and methodology from app manifestos, privacy policies, vulnerability analysis and user reviews were described. Results from a case study on ten well-known finance applications operating in Nigeria were provided in order to assess our methodology. Our findings showed distinct patterns regarding the possible privacy implications of apps, with some of the apps in the data set infringing fundamental privacy principles. The case study’s findings reveal significant differences that can guide users in making relevant app choices.
Android应用的多边隐私影响分析方法
大多数人的私人生活都可以被智能手机应用程序监控。应用程序有可能侵入私人空间,访问和绘制社交互动,跟踪用户的位置,并跟踪他们的在线活动。我们感兴趣的是一个特定的应用程序可以和试图在智能手机上检索的数据量。智能手机应用的隐私友好性通常是基于单一来源分析来评估的,这通常不会对应用的实际隐私威胁提供彻底的评估。为了分析Android应用程序的隐私,本研究提出了一种多源方法。我们的数据集和方法从应用程序宣言,隐私政策,漏洞分析和用户评论进行了描述。为了评估我们的方法,提供了对在尼日利亚运营的十个知名金融应用程序的案例研究结果。我们的研究结果显示了应用程序可能涉及隐私的不同模式,数据集中的一些应用程序违反了基本的隐私原则。案例研究的结果揭示了显著的差异,可以指导用户做出相关的应用选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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