Control yourself: on user control of privacy settings using personalization and privacy panel on smartphones

Zhou, Yun; Piekarska, Marta; Raake, Alexander; Xu, Tao; Wu, Xiaojun; Dong, Bei

doi:10.1016/j.procs.2017.05.300

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Zhou et al [144] accessed the gap between users' desire of privacy control and the actual privacy setting functions provided by mobile app systems. Through a simple lab survey consisting of 26 users, three important facts had been concluded: 1) personal privacy protection is still an important factor that influences the users to choose their smartphones; 2) although smartphone nowadays provides more functions protecting user privacy through complex user interface, people are not well adapted to those new functions; and 3) Sorting methods, as well as recommendation systems are still useful to assist users to protect their private data.…”

Section: Service Providermentioning

confidence: 99%

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Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

et al. 2019

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Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains as a key concern during the sharing service development process. While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of review for privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this study, we survey and evaluate existing and emerging privacy issues relating to sharing services from various perspectives. Differing from existing similar works on surveying sharing practices in various fields, our work comprehensively covers six branches of sharing services in the cyberenabled world, and selects solutions mostly from the recent five to six years. We conclude the issues and solutions from three perspectives, namely, from users', platforms' and service providers' perspectives. Hot topics and less discussed (cold) topics are identified, which provides hints to researchers for their future studies.

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Section: Service Providermentioning

confidence: 99%

“…The proposed strategy is only tested on one single mobile app Zhou et al [144] 2017 accessed the gap between users' desire of privacy control and the actual privacy setting functions provided by mobile app systems A simple lab survey consisting of 26 users…”

Section: A Crowdsourcing Strategymentioning

confidence: 99%

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

et al. 2019

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“…Other metadata such as app version, number of downloads, details of the app provider, and user feedback are also shown to app users. Users can compare an app's functions and permissions (Android's tool to protect user data privacy and device security [37]) to assess its benefits and risks and decide whether they should install the app or not [38]. If the app providers are not transparent in communicating app descriptions and data and device feature requirements, they can lure users into installing the apps and affect their privacy and device security.…”

Section: Transparency and Accountability In Android Appsmentioning

confidence: 99%

“…A need to consider app description and given permissions concurrently to establish a relationship makes it difficult from a problem-solving perspective [91]. However, this is exactly what is expected from a smartphone user in the privacy self-management model followed in Android devices [38,92]. In User Study 2, we followed a controlled approach and obtained a better response rate (99%).…”

Section: Limitationsmentioning

confidence: 99%

On Transparency and Accountability of Smart Assistants in Smart Cities

et al. 2019

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Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

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“…Large amount of users' personal data are produced and associated with users' activities on mobile devices. This increases the issue of privacy at different levels [1]. In addition, users' personal information has become the currency that is paid by mobile apps' users for several mobile services, such as entertainment and social networking apps.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Users' Disclosure Decisions in Android Runtime Permissions Model

Baalous

Poet

2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Today, Android users are faced with several permissions' screens asking to access their personal information when using Android apps. In fact, Android users have to balance several considerations when choosing to grant or deny these data collection activities. Hence, it is important to understand how users' decisions are made and what factors play a role in users' decisions. A number of studies on the permissions' screens of Android devices have reported users discomfort and misunderstanding of the permission system. However, most studies were carried out on the old permission system where all permissions are presented at installation time, and the user has to either accept all the permissions or stop the installation. With the new permission system started with Android version 6.0 and higher, permissions are presented differently at run time. In this work, we aim to study users' disclosure decisions with the new run time system on Android. We have modeled users' disclosure decisions from three perspectives: dangerous permission type, clarity of rationale, and clarity of context. The study has been conducted on Amazon Mechanical Turk. The results show that dangerous permission type as well as clarity of the context have a statistical significant effect on users' disclosure decisions. On the other hand, clarity of dangerous permission's rationale does not contribute significantly to users' decisions. These findings shed light upon important factors that users consider in making privacy decisions in the new Android run time model. Such factors should be taken into account by Android apps developers when requesting access to users' private information.

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Control yourself: on user control of privacy settings using personalization and privacy panel on smartphones

Cited by 10 publications

References 12 publications

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

On Transparency and Accountability of Smart Assistants in Smart Cities

Factors Affecting Users' Disclosure Decisions in Android Runtime Permissions Model

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