Mobile App Privacy Research Articles

How Usable Are iOS App Privacy Labels?

Standardized privacy labels that succinctly summarize those data practices that people are most commonly concerned about offer the promise of providing users with more effective privacy notices than full-length privacy policies. With their introduction by Apple in iOS 14 and Google’s recent adoption in its Play Store, mobile app privacy labels are for the first time available at scale to users. We report the first indepth interview study with 24 lay iPhone users to investigate their experiences, understanding, and perceptions of Apple’s privacy labels. We uncovered misunderstandings of and dissatisfaction with the iOS privacy labels that hinder their effectiveness, including confusing structure, unfamiliar terms, and disconnection from permission settings and controls. We identify areas where app privacy labels might be improved and propose suggestions to address shortcomings to make them more understandable, usable, and useful.

Developers Say the Darnedest Things: Privacy Compliance Processes Followed by Developers of Child-Directed Apps

We investigate the privacy compliance processes followed by developers of child-directed mobile apps. While children’s online privacy laws have existed for decades in the US, prior research found relatively low rates of compliance. Yet, little is known about how compliance issues come to exist and how compliance processes can be improved to address them. Our results, based on surveys (n = 127) and interviews (n = 27), suggest that most developers rely on app markets to identify privacy issues, they lack complete understandings of the third-party SDKs they integrate, and they find it challenging to ensure that these SDKs are kept upto-date and privacy-related options are configured correctly. As a result, we find that well-resourced app developers outsource most compliance decisions to auditing services, and that smaller developers follow “best-effort” models, by assuming that their apps are compliant so long as they have not been rejected by app markets. We highlight the need for usable tools that help developers identify and fix mobile app privacy issues.

The privacy protection behaviours of the mobile app users: exploring the role of neuroticism and protection motivation theory

ABSTRACT Unprecedented and aggressive data access and transmission practices employed by vendors and mobile app developers have aggravated mobile app users’ privacy invasion and privacy concerns. Mobile apps increasingly collect data about users’ behaviour, personal preferences, location, and other personally-identifying information. Often, these data are collected without users’ permission, are unnecessary for the mobile app’s functionality, and are used for unauthorised purposes. We propose a comprehensive but parsimonious research model by drawing upon the protection motivation theory, the theory of planned behaviour, and personality traits literature. The study empirically examines the factors that influence mobile app users’ privacy concerns and how these concerns drive users’ intention and behaviour to use mobile apps. The findings indicate that the threat appraisal has a strong effect on users’ privacy concerns and the personality trait of neuroticism has a substantial effect on perceived vulnerability and perceived severity. Results also indicate that privacy protection behaviours are influenced by users’ coping appraisal and intention to protect their privacy. Our work contributes to the privacy protection behaviour and mobile app privacy literature. We discuss the implications of the results for researchers and practitioners.

From Parental Control to Joint Family Oversight

Our research aims to highlight and alleviate the complex tensions around online safety, privacy, and smartphone usage in families so that parents and teens can work together to better manage mobile privacy and security-related risks. We developed a mobile application ("app") for Community Oversight of Privacy and Security ("CO-oPS") and had parents and teens assess whether it would be applicable for use with their families. CO-oPS is an Android app that allows a group of users to co-monitor the apps installed on one another's devices and the privacy permissions granted to those apps. We conducted a study with 19 parent-teen (ages 13-17) pairs to understand how they currently managed mobile safety and app privacy within their family and then had them install, use, and evaluate the CO-oPS app. We found that both parents and teens gave little consideration to online safety and privacy before installing new apps or granting privacy permissions. When using CO-oPS, participants liked how the app increased transparency into one another's devices in a way that facilitated communication, but were less inclined to use features for in-app messaging or to hide apps from one another. Key themes related to power imbalances between parents and teens surfaced that made co-management challenging. Parents were more open to collaborative oversight than teens, who felt that it was not their place to monitor their parents, even though both often believed parents lacked the technological expertise to monitor themselves. Our study sheds light on why collaborative practices for managing online safety and privacy within families may be beneficial but also quite difficult to implement in practice. We provide recommendations for overcoming these challenges based on the insights gained from our study.

Mobile app privacy in software engineering research: A systematic mapping study

Abstract Context: Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional privacy concerns among app users. Objective: The research on mobile app privacy has experienced rapid growth over the past decade. This line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this paper, we conduct a systematic mapping study of this body of research. Our objectives are to a) explore trends in SE app privacy research, b) categorize existing evidence, and c) identify potential directions for future research. Method: A systematic mapping study of 59 Software Engineering (SE) primary studies on mobile app privacy. Our scope is studies published in software engineering venues between 2008 and 2018. Results: Our results show that existing literature can be divided into four main categories: privacy policy, requirements, user perspective, and leak detection. Furthermore, our survey reveals an imbalance between these categories—the majority of existing research focuses on proposing tools for detecting privacy leaks, with fewer studies targeting privacy requirements and policy and even fewer on user perspective. Conclusions: Our survey exposes several gaps in existing research and suggests areas for improvement.

Investigating User Perceptions of Mobile App Privacy

Mobile devices and third-party applications are used by over 4.5 billion people worldwide. Third-party applications often request or even require authorized access to personal information through mobile device components. Application developers explain the need for access in their privacy policies, yet many users are concerned about the privacy implications of allowing access to their personal information. This article explores how user perceptions of privacy affect user sentiment by analyzing over five million user-submitted text reviews and star ratings collected over a four-year period. The authors use supervised machine learning to classify privacy and non-privacy-related reviews. The authors then use natural language processing sentiment analysis to compare differences between the groups. Additionally, the article explores various aspects of both privacy and non-privacy-related reviews using self-reported measurements such as star rating and helpfulness tags.

The Best of Both Worlds: Mitigating Trade-offs Between Accuracy and User Burden in Capturing Mobile App Privacy Preferences

Abstract In today’s data-centric economy, data flows are increasingly diverse and complex. This is best exemplified by mobile apps, which are given access to an increasing number of sensitive APIs. Mobile operating systems have attempted to balance the introduction of sensitive APIs with a growing collection of permission settings, which users can grant or deny. The challenge is that the number of settings has become unmanageable. Yet research also shows that existing settings continue to fall short when it comes to accurately capturing people’s privacy preferences. An example is the inability to control mobile app permissions based on the purpose for which an app is requesting access to sensitive data. In short, while users are already overwhelmed, accurately capturing their privacy preferences would require the introduction of an even greater number of settings. A promising approach to mitigating this trade-off lies in using machine learning to generate setting recommendations or bundle some settings. This article is the first of its kind to offer a quantitative assessment of how machine learning can help mitigate this trade-off, focusing on mobile app permissions. Results suggest that it is indeed possible to more accurately capture people’s privacy preferences while also reducing user burden.

Going global: Comparing Chinese mobile applications’ data and user privacy governance at home and abroad

We examine and compare data and privacy governance by four China-based mobile applications and their international versions: Baidu, Toutiao and its international version TopBuzz, Douyin and its international version TikTok, and WeChat. Together, these four applications represent popular Chinese apps branching into diverse overseas markets such as Europe, Brazil, North America, and Southeast Asia. We first present an overview of the ownership, functions, business models and strategies of the reviewed apps. To study the app's interface design, we employ the walkthrough method to examine privacy features during the account registration and deletion stages in app usage. Lastly, we conducted content analysis of the terms of service and privacy policies to establish the app's data collection, storage, transfer, use, and disclosure measures. Our analysis showed variations across apps and within the Chinese and international-facing versions in their data and privacy governance in app design and policies. Baidu has the most unsatisfactory data and privacy protection measures, while ByteDance's TikTok/Douyin and TopBuzz/Toutiao offer more comprehensive user protection from different jurisdictions. Moreover, this paper highlights the role of platform owners (e.g., Google and Apple) in gatekeeping mobile app privacy standards and the role of the state in imposing a data protection framework on overseas versions of China-based mobile apps.

Terms of public service: Framing mobile privacy discourses

Engaging normative theories of the press and research examining the evolution of privacy coverage, this study examines press coverage of mobile app privacy issues between 2013 and 2016. The research sheds light on how the press frames privacy concerns within the mobile app context. Since such coverage can define the norms circumscribing the flows of users’ personal information, this study contributes to the debate about the role of the press in alerting the public to privacy issues that carry significant public interest implications. Ultimately, mobile privacy coverage favors certain solutions over others, emphasizes privacy tradeoffs over privacy rights, and balances user powerlessness with mobile app convenience and innovation, with implications for privacy discourses in public and policy arenas.

MAPS: Scaling Privacy Compliance Analysis to a Million Apps

Abstract The app economy is largely reliant on data collection as its primary revenue model. To comply with legal requirements, app developers are often obligated to notify users of their privacy practices in privacy policies. However, prior research has suggested that many developers are not accurately disclosing their apps’ privacy practices. Evaluating discrepancies between apps’ code and privacy policies enables the identification of potential compliance issues. In this study, we introduce the Mobile App Privacy System (MAPS) for conducting an extensive privacy census of Android apps. We designed a pipeline for retrieving and analyzing large app populations based on code analysis and machine learning techniques. In its first application, we conduct a privacy evaluation for a set of 1,035,853 Android apps from the Google Play Store. We find broad evidence of potential non-compliance. Many apps do not have a privacy policy to begin with. Policies that do exist are often silent on the practices performed by apps. For example, 12.1% of apps have at least one location-related potential compliance issue. We hope that our extensive analysis will motivate app stores, government regulators, and app developers to more effectively review apps for potential compliance issues.

Predicting Users Mobile App Privacy Preferences

Home users are using a wide and increasing range of different technologies, devices, platforms, applications and services every day. In parallel, home users are also installing and using an enormous number of apps, which collect and share a large amount of data. Users are also often unaware of what information apps collect about them, which is really valuable and sensitive for them. Therefore, users are becoming increasingly concerned about their personal information that is stored in these apps. While most mobile operating systems such as Android and iOS provide some privacy safeguards for users, it is unrealistic to manage and control a large volume of data. Accordingly, there is a need for a new technique, which has the ability to predict many of a user’s mobile app privacy preferences. A major contribution of this work is to utilise different machine learning techniques for assigning users to the privacy profiles that most closely capture their privacy preferences. Applying privacy profiles as default settings for initial interfaces could significantly reduce the burden and frustration of the user. The result shows that it’s possible to reduce the user’s burden from 46 to 10 questions by achieving 86% accuracy, which indicates that it’s possible to predict many of a user’s mobile app privacy preferences by asking the user a small number of questions.

Losing Control to Data-Hungry Apps: A Mixed-Methods Approach to Mobile App Privacy

Personal data from mobile apps are increasingly impacting users’ lives and privacy perceptions. However, there is a scarcity of research addressing the combination of (1) individual perceptions of mobile app privacy, (2) actual dataflows in apps, and (3) how such perceptions and dataflows relate to actual privacy policies and terms of use in mobile apps. To address these limitations, we conducted an innovative mixed-methods study including a representative user survey in Norway, an analysis of personal dataflows in apps, and content analysis of privacy policies of 21 popular, free Android mobile apps. Our findings show that more than half the respondents in the user survey repeatedly had refrained from downloading or using apps to avoid sharing personal data. Our analysis of dataflows applied a novel methodology measuring activity in the apps over time (48 hr). The investigation showed that 19 of the 21 apps investigated transmitted personal data to a total of approximately 600 different primary and third-party domains. From an European perspective, it is particularly noteworthy that most of these domains were associated with tech companies in the United States, where privacy laws are less strict than companies operating from Europe. The investigation further revealed that some apps by default track and share user data continuously, even when the app is not in use. For some of these, the terms of use provided with the apps did not inform the users about the actual tracking practice. A comparison of terms of use as provided in the studied apps with actual person dataflows as identified in the analysis disclosed that three of the apps shared data in violation with their provided terms of use. A possible solution for the mobile app industry, to strengthen user trust, is privacy by design through opt-in data sharing with the service and third parties and more granular information on personal data sharing practices. Also, based on the findings from this study, we suggest specific visualizations to enhance transparency of personal dataflows in mobile apps. A methodological contribution is that a mixed-methods approach strengthens our understanding of the complexity of privacy issues in mobile apps.

Determinants of Personal Health Information Disclosure

This study explored the factors that affect personal health information (PHI) disclosure via a mobile application (app) in Thailand. Since mobile apps are increasingly popular, as is the Thai people's concern on their health condition, many mobile app service providers want to know which factors would persuade customers to reveal their PHI via mobile apps. This research model was, therefore, developed and included the six factors of: personalized service, self-presentation, mobile app reputation, familiarity, perceived benefits and privacy concerns. The hypotheses were tested by structural equation modeling using the questionnaire responses from 294 valid subjects. Surprisingly, privacy concern was not significantly negatively related to the intention to disclose PHI. However, the significance effect of the perceived benefit, personalized service and self-presentation were consistent with previous studies. In addition, the respondents were willing to reveal different personal information in different situations. The implication of the result will shed light on the development of a healthcare mobile app service provider.

Improving Smartphone Security and Reliability

Users are increasingly relying on smartphones, hence concerns such as mobile app security, privacy, and correctness have become increasingly pressing. Software analysis has been successful in tackling many such concerns, albeit on other platforms, such as desktop and server. To fill this gap, he have developed infrastructural tools that permit a wide range of software analyses for the Android smartphone platform. Developing these tools has required surmounting many challenges unique to the smartphone platform: dealing with input non-determinism in sensor-oriented apps, non-standard control ow, low-overhead yet high-fidelity record-and-replay. Our tools can analyze substantial, widely-popular apps running directly on smartphones, and do not require access to the app’s source code. We will first present two tools (automated exploration, record-and-replay) that increase Android app reliability by allowing apps to be explored automatically, and bugs replayed or isolated. Next, we present several security applications of our infrastructure: a permission evolution study on the Android ecosystem; understanding and quantifying the risk posed by URL accesses in benign and malicious apps; app profiling to summarize app behavior; and Moving Target Defense for thwarting attacks.

How Dangerous Are Your Smartphones? App Usage Recommendation with Privacy Preserving

With the rapid proliferation of mobile devices, explosive mobile applications (apps) are developed in the past few years. However, the functions of mobile apps are varied and the designs of them are not well understood by end users, especially the activities and functions related to user privacy. Therefore, understanding how much danger of mobile apps with respect to privacy violation to mobile users is becomes a critical issue when people use mobile devices. In this paper, we evaluate the mobile app privacy violation of mobile users by computing the danger coefficient. In order to help people reduce the privacy leakage, we combine both the user preference to mobile apps and the privacy risk of apps and propose a mobile app usage recommendation method named AppURank to recommend the secure apps with the same function as the “dangerous” one for people use. The evaluation results show that our recommendation can reduce the privacy leakage by 50%.

Mobile health applications for asthma

Mobile health applications for asthma

Availability and quality of mobile health app privacy policies.

Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35,000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy.

The determinants of increasing privacy concerns in the mobile apps

This study identifies the determinants of increasing privacy concerns in the mobile apps. The privacy affecting factors will be divided into the 3 categories on the basis of both users' and developers' perspective. First, this study explores whether the users' seeking value affects concern of the mobile apps privacy. Second, gender gap on the mobile privacy sensitivity will be tested. Third, this study identifies the meaning of apps developers' providing toolkit in the users' sensitivity on the privacy. The result showed that innovation among the customer's characteristics was identified the most significant factor to affect the mobile app privacy sensitivity. Ethical norm related variables (ethical identity, subjective norm and utilitarian value) were also identified as the meaningful variables in the mobile app privacy. Toolkits provided by app developers are also appeared a significant variable to affect the customer's app privacy sensitivity negatively. Finally gender also appears the meaningful factor in the mobile app privacy sensitivity.