App Privacy Policies Research Articles

Intimate harms and menstrual cycle tracking apps

Menstrual cycle tracking applications (‘apps’) are smartphone or tablet apps that allow users to log data pertaining to their period. Using a lens of privacy focussed on intimacy, it will be argued that the control-based harms and intimate harms emerging from these apps require moving from an information privacy law model based on control to one that acknowledges the deeper connection between intimacy and privacy. We examine the privacy policies of 20 menstrual cycle tracking apps to investigate how the control-based protections of the Privacy Act apply. Our findings demonstrate that there are many deficiencies in app privacy policies which give rise to critical questioning about the application of the Australian Privacy Act’s control approach. We argue that the current gender-agnostic approach of information privacy law's control approach does not adequately protect app users and their intimate information. Intimate harms rethink the application of information privacy law by extending its reach beyond the traditional control harms contemplated by the Act and examine how menstrual cycle tracking apps disrupt users’ intimate spheres and relationships. To adequately protect app users from these deeper intimate harms, we contend that information privacy law moves beyond the procedural-based control approach to an information privacy model that is relational, context-dependant and acknowledges the connection between intimacy and privacy.

APP隐私政策知情同意规则的适用困境与应对路径

APP隐私政策知情同意规则的适用困境与应对路径

Scoping review of data privacy risks in COVID-19 apps with digital vaccination certifications.

The goal was to review mobile apps with COVID-19 digital vaccination certificates between November 2022 and March 2023 and evaluate: (a) compliance with the WHO Proof of Vaccination Scenario requirements, (b) risk levels of app permissions using a Permission Accumulated Risk Score (PARS), and (c) readability and transparency of the app's privacy policies using a Privacy Transparency Index (PTI) score. We found 49 mobile apps with COVID-19 digital vaccination certificates from across 32 countries. Most apps were developed by governments (37/49, 75.51%). We discovered a high positive correlation between the country-wide app total installs and the people vaccinated with at least one dose in the country (r = 0.93, P = <.001). Most apps (97.96%) had sources of information available for compliance with WHO Proof of Vaccination Scenario requirements. Only two apps included all the required data items, while most apps (75%) included five or more data out of nine items. We found that most (97.96%) apps had a Google Play link to generate the Exodus platform permission report, and most (95.92%) apps had an associated privacy policy available. We identified 80 unique permissions; some (23.75%) were dangerous or special. We also found 28 types of trackers. The average PARS was 28.58 (IQR 23.25, range 15-38.25). Most of the apps' privacy policies documents were difficult or very difficult to read (median grade level 14, IQR 2.6, range 13-15.6). The average PTI was 50.43 (SD 14.73; range 22.5-75). In conclusion, higher compliance with the WHO Proof of Vaccination Scenario requirements is desirable to support interoperability. Developers should limit the number of permissions for essential needs and disclose their purpose. Developers should write privacy policies that a wider audience can understand.

Impact of Users&amp;rsquo; Comprehension of the Privacy Policy of FemTech Apps on their Information Disclosure Intentions: The Mediating Effects of Privacy Fatigue and Privacy Data Control

FemTech stands for female technology, which encompasses a wide range of software, products, and services designed to improve women's health through technology. Personal data used in this type of app are intimate, which highlights the importance of privacy, and therefore, comprehensibility of the privacy policies of such apps. This study explores the effect of user comprehension of privacy policies on their information disclosure intention through mediators such as privacy fatigue and privacy data control, which have rarely been studied. A scenario-based questionnaire was used to collect data from 236 females and SMART PLS 4.0 was used to conduct the analysis. Findings indicate that both mediators have an indirect-only mediation effect; however, the direction of the impact of privacy fatigue on disclosure intention was opposite to what was hypothesised. Privacy data control was found to be the stronger mediator. This study entails implications for privacy policymakers and mobile health application providers.

A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study.

Health care services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of an mHealth app. Privacy consists of several components, including organizational, technical, and legal safeguards. Within legal safeguards, giving transparent information to the users on how their data are handled is crucial. This information is usually disclosed to users through the privacy policy document. Assessing the quality of a privacy policy is a complex task and several scales supporting this process have been proposed in the literature. However, these scales are heterogeneous and even not very objective. In our previous study, we proposed a checklist of items guiding the assessment of the quality of an mHealth app privacy policy, based on the General Data Protection Regulation. To refine the robustness of our General Data Protection Regulation-based privacy scale to assess the quality of an mHealth app privacy policy, to identify new items, and to assign weights for every item in the scale. A two-round modified eDelphi study was conducted involving a privacy expert panel. After the Delphi process, all the items in the scale were considered "important" or "very important" (4 and 5 in a 5-point Likert scale, respectively) by most of the experts. One of the original items was suggested to be reworded, while eight tentative items were suggested. Only two of them were finally added after Round 2. Eleven of the 16 items in the scale were considered "very important" (weight of 1), while the other 5 were considered "important" (weight of 0.5). The Benjumea privacy scale is a new robust tool to assess the quality of an mHealth app privacy policy, providing a deeper and complementary analysis to other scales. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps.

Medical Information Protection in Internet Hospital Apps in China: Scale Development and Content Analysis.

Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical services and enhanced accessibility. However, increasing concerns about personal information (PI) and strict legal compliance requirements necessitate privacy assessments for these platforms. Guided by the theory of contextual integrity, this study investigates the regulatory compliance of privacy policies for internet hospital apps in the mainland of China. In this paper, we aim to evaluate the regulatory compliance of privacy policies of internet hospital apps in the mainland of China and offer recommendations for improvement. We obtained 59 internet hospital apps on November 7, 2023, and reviewed 52 privacy policies available between November 8 and 23, 2023. We developed a 3-level indicator scale based on the information processing activities, as stipulated in relevant regulations. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 70 level-3 indicators. The mean compliance score of the 52 assessed apps was 73/100 (SD 22.4%), revealing a varied spectrum of compliance. Sensitive PI protection compliance (mean 73.9%, SD 24.2%) lagged behind general PI protection (mean 90.4%, SD 14.7%), with only 12 apps requiring separate consent for processing sensitive PI (mean 73.9%, SD 24.2%). Although most apps (n=41, 79%) committed to supervising subcontractors, only a quarter (n=13, 25%) required users' explicit consent for subcontracting activities. Concerning PI storage security (mean 71.2%, SD 29.3%) and incident management (mean 71.8%, SD 36.6%), half of the assessed apps (n=27, 52%) committed to bear corresponding legal responsibility, whereas fewer than half (n=24, 46%) specified the security level obtained. Most privacy policies stated the PI retention period (n=40, 77%) and instances of PI deletion or anonymization (n=41, 79%), but fewer (n=20, 38.5%) committed to prompt third-party PI deletion. Most apps delineated various individual rights, but only a fraction addressed the rights to obtain copies (n=22, 42%) or to refuse advertisement based on automated decision-making (n=13, 25%). Significant deficiencies remained in regular compliance audits (mean 11.5%, SD 37.8%), impact assessments (mean 13.5%, SD 15.2%), and PI officer disclosure (mean 48.1%, SD 49.3%). Our analysis revealed both strengths and significant shortcomings in the compliance of internet hospital apps' privacy policies with relevant regulations. As China continues to implement internet hospital apps, it should ensure the informed consent of users for PI processing activities, enhance compliance levels of relevant privacy policies, and fortify PI protection enforcement across the information processing stages.

Assessment of App Store Description and Privacy Policy to Explore Ethical and Safety Concerns Associated with the Use of Mental Health Apps for Depression.

Mental Health apps (MH apps) could help address the huge unmet mental health care need of developing countries. This study aimed to explore potential ethical, data safety, and privacy issues associated with using MH apps for depression. A cross-sectional assessment of the top 50 MH apps (by Google Play store search result ordering) for depression available in India was conducted in November 2021. Most apps were listed under the category of health and fitness (54%). The median number of total and dangerous permissions requested at the time of download was nine and three, respectively. Privacy policy in English was available for 76%. The average length of the privacy policy was 2171 words, and the mean Flesch-Kincaid reading grade level was 12 (much higher than the recommended cut-off of eight). Important features relevant to safeguarding consumer confidentiality, including names of third parties with which user data could be shared (42%), explicit consent before sharing data with third parties (16%), and assurance regarding the collection of de-identifiable data (11%), were missing from the majority of privacy policies. There is an urgent need to improve the accessibility and usability of privacy policies by app developers, with the active involvement of other stakeholders.

Read and accepted? Scoping the cognitive accessibility of privacy policies of health apps and websites in three European countries.

Trust and accessibility are vital to adoption of health and wellness apps. This research scoped three elements of cognitive accessibility of health app privacy policies: availability, ease of navigation, and readability. For this cross-sectional study, quantitative data collected in the Netherlands, Sweden, and the United Kingdom included: whether privacy information was in a country's official language (availability); number of distracting visual elements (ease of navigation); word count and Common European Framework of Reference (CEFR) reading level (readability). Health app privacy policies were compared to policies from a purposively selected sample of websites, and to benchmarks, including CEFR reading level B1. Health app privacy policies were less often available in countries' official languages compared to sampled websites (Chi-Square [1, 180] = 57.470, p < 0.001) but contained fewer distracting visual elements. More UK privacy policies were in the country's official language, whereas Swedish privacy policies contained fewest words and fewest potentially distracting design elements. Only one privacy policy met the CEFR reading level benchmark. Lack of privacy information in non-Anglophone app-users' native languages and high reading levels may be major barriers to cognitive accessibility. Web and app developers should consider recommendations arising from this study, to stimulate trust in and adoption of health and wellness apps.

A Multilateral Privacy Impact Analysis Method for Android Applications

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.

Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store.

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps' privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.

Evaluating the Privacy Policy of Android Apps: A Privacy Policy Compliance Study for Popular Apps in China and Europe

Recently, with the increase in the market share of the Android system and the sharp increase in the number of Android mobile apps, many countries and regions have successively launched laws and regulations related to data security. The EU’s GDPR and China’s Information Security Technology-Personal Information Security Specification are two of the most important bills, affecting vast areas and large populations. Both regulations impose requirements on privacy policy specifications for Android apps. With these requirements, however, apps’ privacy policies have become larger. Researchers have conducted studies on whether the actual privacy behavior of apps conforms to their privacy policy description but have not focused on compliance with the privacy policy itself. In this paper, we propose evaluation metrics for privacy policy compliance and evaluate popular apps by analyzing privacy policies and apps. We applied our method to 1,000 apps from the Google Play Store in Europe and 1,000 apps from the Tencent Appstore in China. We detected a number of app privacy policy noncompliance issues and discovered a number of privacy issues with third-party services and third-party libraries.

Privacy, Data Sharing, and Data Security Policies of Women's mHealth Apps: Scoping Review and Content Analysis.

BackgroundWomen’s mobile health (mHealth) is a growing phenomenon in the mobile app global market. An increasing number of women worldwide use apps geared to female audiences (female technology). Given the often private and sensitive nature of the data collected by such apps, an ethical assessment from the perspective of data privacy, sharing, and security policies is warranted.ObjectiveThe purpose of this scoping review and content analysis was to assess the privacy policies, data sharing, and security policies of women’s mHealth apps on the current international market (the App Store on the Apple operating system [iOS] and Google Play on the Android system).MethodsWe reviewed the 23 most popular women’s mHealth apps on the market by focusing on publicly available apps on the App Store and Google Play. The 23 downloaded apps were assessed manually by 2 independent reviewers against a variety of user data privacy, data sharing, and security assessment criteria.ResultsAll 23 apps collected personal health-related data. All apps allowed behavioral tracking, and 61% (14/23) of the apps allowed location tracking. Of the 23 apps, only 16 (70%) displayed a privacy policy, 12 (52%) requested consent from users, and 1 (4%) had a pseudoconsent. In addition, 13% (3/23) of the apps collected data before obtaining consent. Most apps (20/23, 87%) shared user data with third parties, and data sharing information could not be obtained for the 13% (3/23) remaining apps. Of the 23 apps, only 13 (57%) provided users with information on data security.ConclusionsMany of the most popular women’s mHealth apps on the market have poor data privacy, sharing, and security standards. Although regulations exist, such as the European Union General Data Protection Regulation, current practices do not follow them. The failure of the assessed women’s mHealth apps to meet basic data privacy, sharing, and security standards is not ethically or legally acceptable.

Pulse Oximeter App Privacy Policies During COVID-19: Scoping Assessment

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

PermPress: Machine Learning-Based Pipeline to Evaluate Permissions in App Privacy Policies

Privacy laws and app stores (e.g., Google Play Store) require mobile apps to have transparent privacy policies to disclose sensitive actions and data collection, such as accessing the phonebook, camera, storage, GPS, and microphone. However, many mobile apps do not accurately disclose their sensitive data access that requires sensitive (&#x2019;dangerous&#x2019;) permissions. Thus, analyzing discrepancies between apps&#x2019; permissions and privacy policies facilitates the identification of compliance issues upon which privacy regulators and marketplace operators can act. This paper proposes <i>PermPress</i> &#x2013; an automated machine-learning system to evaluate an Android app&#x2019;s permission-completeness, i.e., whether its privacy policy matches its dangerous permissions. <i>PermPress</i> combines machine learning techniques with human annotation of privacy policies to establish whether app policies contain permission-relevant information. <i>PermPress</i> leverages MPP-270, an annotated policy corpus, for establishing a gold standard dataset of permission completeness. This corpus shows that only 31% of apps disclose all dangerous permissions in privacy policies. By leveraging the annotated dataset and machine learning techniques, <i>PermPress</i> achieves an AUC score of 0.92 in predicting the permission-completeness of apps. A large-scale evaluation of 164, 156 Android apps shows that, on average, 7% of apps do not disclose more than half of their declared dangerous permissions in privacy policies, whereas 60% of apps omit to disclose at least one dangerous permission-related data collection in privacy policies. This paper&#x2019;s investigation uncovers the non-transparent state of app privacy policies and highlights the need to standardize app privacy policies&#x2019; compliance and completeness checking process.

Privacy policies of IoT-based healthcare: an empirical analysis study of the data collection practices of existing IoT medical applications

In healthcare, the internet of things (IoT) has accelerated the process of gathering patients' data. However, data processing is a significant violation of patients' privacy. This research critically examines to what extent the privacy policies of IoT medical apps provide sufficient details about their data collection practices with respect to personal data. It aims to explore the various medical data collected by IoT medical apps. An empirical analysis investigated the privacy policies of 21 of IoT medical apps. The findings revealed that these apps had provided sufficient details related to some of the data collection practices. However, details related to the security of personal data were more general. The findings indicate that most of these apps offer no guarantee regarding securing personal data. Such results open new research to be extended to validate to what extent the service providers are committed to what is explained in their privacy policies.

Terms and conditions apply: Critical issues for readability and jargon in mental health depression apps

BackgroundMental health services are turning to technology to ease the resource burden, but privacy policies are hard to understand potentially compromising consent for people with mental health problems. The FDA recommends a reading grade of 8. ObjectiveTo investigate and improve the accessibility and acceptability of mental health depression app privacy policies. MethodsA mixed methods study using quantitative and qualitative data to improve the accessibility of app privacy policies. Service users completed assessments and focus groups to provide information on ways to improve privacy policy accessibility, including identifying and rewording jargon. This was supplemented by comparisons of mental health depression apps with social media, music and finance apps using readability analyses and examining whether GDPR affected accessibility. ResultsService users provided a detailed framework for increasing accessibility that emphasised having critical information for consent. Quantitatively, most app privacy policies were too long and complicated for ensuring informed consent (mental health apps mean reading grade = 13.1 (SD = 2.44)). Their reading grades were no different to those for other services. Only 3 mental health apps had a grade 8 or less and 99% contained service user identified jargon. Mental health app privacy policies produced for GDPR weren't more readable and were longer. ConclusionsApps specifically aimed at people with mental health difficulties are not accessible and even those that fulfilled the FDA's recommendation for reading grade contained jargon words. Developers and designers can increase accessibility by following a few rules and should, before launching, check whether the privacy policy can be understood.

Investigation into and Reflection of User Privacy Policies of Reading Apps

Investigation into and Reflection of User Privacy Policies of Reading Apps

AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing

Thanks to the popularity of personal mobile devices, more and more of the different types of private content, such as images and videos, are shared on social networking applications. While content sharing may be an effective practice to enhance social relationships, it is also a source of relevant privacy issues. Unfortunately, users find it difficult to understanding the terms and implications of the privacy policies of apps and services. Moreover, taking privacy decisions about content sharing on social networks is cumbersome and prone to errors that could determine privacy leaks. In this paper, we propose two techniques aimed at supporting the user in taking privacy choices about sharing personal content online. Our techniques are based on machine learning and natural language processing to analyze privacy policies, and on computer vision to assist the user in the privacy-conscious sharing of multimedia content. Experiments with real-world data show the potential of our solutions. We also present ongoing work on a system prototype and chatbot for natural language user assistance.

Safe, Sound, and Private: Promoting Data Protection for Students

In an effort to slow the spread of the highly infectious disease coronavirus (COVID-19), school districts around the world closed school buildings, thrusting educators into emergency remote teaching. Although many vendors were quick to release tutorial guides and free trials for their online learning resources, the growing concern over student data protection intensified. A lack of transparency or disregard for privacy in the privacy policies of many apps and websites used by students may allow third parties to generate detailed profiles of students to be used for behavioral advertising, which could cause unforeseen consequences. This article highlights existing federal laws that aim to protect student data, explains the importance of vetting apps and websites used by students, offers guidance to assist schools with the vetting process, and concludes with resources to teach about digital citizenship, including lessons in which students learn how to protect their own data.

Deficiencies in the Disclosures of Privacy Policies

Development of a comprehensive legal privacy framework in the United States should be based on identification of the common deficiencies of privacy policies. We attempt to delineate deficiencies by critically analyzing the privacy policies of mobile apps, application suites, social networks, Internet Service Providers, and Internet-of-Things devices. Whereas many studies have examined readability of privacy policies, few have specifically identified the information that should be provided in privacy policies but is not. Privacy legislation invariably starts a definition of personally identifiable information. We find that privacy policies’ definitions of personally identifiable information are far too restrictive, excluding information that does not itself identify a person but which can be used to reasonably identify a person, and excluding information paired with a device identifier which can be reasonably linked to a person. Legislation should define personally identifiable information to include such information, and should differentiate between information paired with a name versus information paired with a device identifier. Privacy legislation often excludes anonymous and de-identified information from notice and choice requirements. We find that privacy policies’ descriptions of anonymous and de-identified information are far too broad, including information paired with advertising identifiers. Computer science has repeatedly demonstrated that such information is reasonably linkable. Legislation should define these categories of information to align with technological abilities. Legislation should also not exempt de-identified information from notice requirements, to increase transparency. Privacy legislation relies heavily on notice requirements. We find that, because privacy policies’ disclosures of the uses of personal information are disconnected from their disclosures about the types of personal information collected, we are often unable to determine which types of information are used for which purposes. Often, we cannot determine whether location or web browsing history is used solely for functional purposes or also for advertising. Legislation should require the disclosure of the purposes for each type of personal information collected. We also find that, because privacy policies disclosures of sharing of personal information are disconnected from their disclosures about the types of personal information collected, we are often unable to determine which types of information are shared. Legislation should require the disclosure of the types of personal information shared. Finally, privacy legislation relies heavily on user choice. We find that free services often require the collection and sharing of personal information. As a result, users often have no choices. We find that whereas some paid services afford users a wide variety of choices, paid services in less competitive sectors often afford users few choices over use and sharing of personal information for purposes unrelated to the service. As a result, users are often unable to dictate which types of information they wish to allow to be shared, and which types they wish to allow to be used for advertising. Legislation should differentiate between take-it-or-leave it, opt-out, and opt-in approaches based on the type of use and on whether the information is shared. Congress should consider whether user choices should be affected by the presence of market power.