Visualizing Privacy Risks of Mobile Applications through a Privacy Meter

Kang, Jae-Won; Kim, Hyoungshick; Cheong, Yun Gyung; Huh, Jun Ho

doi:10.1007/978-3-319-17533-1_37

Cited by 12 publications

(11 citation statements)

References 11 publications

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“…In a study on users' app recommendation decisions, Kang et al (2015) proposed Privacy Meter, a meter presented as a slider bar to indicate risks. The results of a role-playing laboratory study showed that the privacy meter is most effective in visualising risks in comparison with the Google Play store permissions displays (before and after version 4.8.20) and the privacy facts checklist of Kelley et al (2013).…”

Section: Privacy Information Communicationmentioning

confidence: 99%

“…Second, we tested the effects of generation fluency and framing in a more realistic app-install environment in which all common app attributes including user ratings and reviews as well as download counts were presented, whereas the work of Choe et al (2013) emphasised user rating and different levels of privacy rating. Third, to design the framing stimuli, inspired by the work of Kang et al (2015) which highlighted the effectiveness of privacy meter in risk communication, we created a new version of the privacy-rating visualisation. We incorporated the notion of making privacy part of the decision-making process suggested by Kelley et al (2013) into our privacy rating design.…”

Section: Privacy Information Communicationmentioning

confidence: 99%

“…We varied the privacy rating on the App Description page in either a positive or negative way while controlling other app attributes to be identical in all experimental conditions. Adopting the concept of privacy meter suggested by Kang et al (2015), we used a slider bar to represent our privacy rating. As shown in Figure 2, our rating slider bar presents a unipolar scale depicting increasing degrees of privacy (or risks) in a positive (or negative) frame.…”

Section: App Interface Designmentioning

confidence: 99%

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Reasoning about privacy in mobile application install decisions: Risk perception and framing

Wah

Teh

Payne

2021

International Journal of Human-Computer Studies

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Data sharing has become prevalent with the rapid growth of mobile technologies. A lack of awareness and understanding of privacy practices often results in the installation of privacy-invasive applications (apps) which could potentially put users' personal data at risk. This study aimed to explore how users' risk perception could be shifted towards more privacy-aware decisions through generation fluency and framing manipulations. It is an online study composed of three components, an experiment and two questionnaires. We manipulated the availability of privacy worries, by asking participants to generate either 2 or 10 privacy worries. Generating 10 worries was experienced as difficult, whereas generating 2 was easy. The difficult experience led to downgraded perception of risk, and consequently increased likelihood of installing a low privacy rated fictional app. Therefore, we suggest that improving generation fluency of privacy concerns could encourage users' adoption of a more conservative judgment strategy when installing an app, safeguarding them against privacy-invasive apps.

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Section: Privacy Information Communicationmentioning

confidence: 99%

Section: Privacy Information Communicationmentioning

confidence: 99%

Section: App Interface Designmentioning

confidence: 99%

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Reasoning about privacy in mobile application install decisions: Risk perception and framing

Wah

Teh

Payne

2021

International Journal of Human-Computer Studies

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“…Having graphical representation for the permissions' statics within a specific category encouraged the users to choose more often apps with a lower number of permissions. A privacy meter was used in [83] to visualize the permissions' statistics through a slider bar which outperformed the existing warning system like Google's permission screens. Visualizing app activities enhances user's awareness and sensitivity to the privacy intrusiveness of mobile applications [84].…”

Section: Android Malware Application Detection and Rankingmentioning

confidence: 99%

Android Application Security Scanning Process

Almomani¹,

Alenezi²

2019

Telecommunication Systems - Principles and Applications of Wireless-Optical Technologies

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This chapter presents the security scanning process for Android applications. The aim is to guide researchers and developers to the core phases/steps required to analyze Android applications, check their trustworthiness, and protect Android users and their devices from being victims to different malware attacks. The scanning process is comprehensive, explaining the main phases and how they are conducted including (a) the download of the apps themselves; (b) Android application package (APK) reverse engineering; (c) app feature extraction, considering both static and dynamic analysis; (d) dataset creation and/or utilization; and (e) data analysis and data mining that result in producing detection systems, classification systems, and ranking systems. Furthermore, this chapter highlights the app features, evaluation metrics, mechanisms and tools, and datasets that are frequently used during the app's security scanning process.

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“…In addition, visualisation as a means for communicating privacy and security measures has been shown to have a positive effect on the trust that the users have in services (Becker et al, 2014). Hence, techniques that allow users to easily grasp the privacy risk associated with the privacy policies and with their own personalised privacy settings, analogous to those proposed in Kang et al (2015), Yee et al (2008), Ghazinour et al (2009) should be also developed and employed in OSNs.…”

Section: An Improved Privacy Policy Modelmentioning

confidence: 99%

Towards improving existing online social networks' privacy policies

Michota

Katsikas

2018

IJIPSI

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The privacy policies of online social network (OSN) service providers are criticised as falling short of satisfying their users' privacy expectations letting huge quantities of their personally identifiable information (PII) exposed to unknown audiences. The purpose of this paper is twofold: to assess the conformance of the privacy policies applied in the five topmost leading OSNs to an internationally acknowledged benchmark such as the ISO 29100:2011 standard, and to propose improvements based on the findings of the assessment. Further, as serious mismatches between these privacy policies and the adherence criteria set out in the ISO 29100:2011 standard were identified, a data lifecycle model is proposed as the basis for an improved OSN privacy policy. A restructuring of the existing policies according to the data lifecycle model will allow them to enjoy characteristics that are known to be important in forming users' perceptions.

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Visualizing Privacy Risks of Mobile Applications through a Privacy Meter

Cited by 12 publications

References 11 publications

Reasoning about privacy in mobile application install decisions: Risk perception and framing

Reasoning about privacy in mobile application install decisions: Risk perception and framing

Android Application Security Scanning Process

Towards improving existing online social networks' privacy policies

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