In recent years, "gig work" platforms have gained popularity as a way for individuals to earn money; as of 2021, 16% of Americans have at some point earned money from such platforms. Despite their popularity and their history of unfair data collection practices and worker safety, little is known about the data collected from workers (and users) by gig platforms and about the privacy dark pattern designs present in their apps. This paper presents an empirical measurement of 16 gig work platforms' data practices in the U.S. We analyze what data is collected by these platforms, and how it is shared and used. Finally, we consider how these practices constitute privacy dark patterns. To that end, we develop a novel combination of methods to address gig-worker-specific challenges in experimentation and data collection, enabling the largest in-depth study of such platforms to date. We find extensive data collection and sharing with 60 third parties—including sharing reversible hashes of worker Social Security Numbers (SSNs)—along with dark patterns that subject workers to greater privacy risk and opportunistically use collected data to nag workers in off-platform messages. We conclude this paper with proposed interdisciplinary mitigations for improving gig worker privacy protections. After we disclosed our SSN-related findings to affected platforms, the platforms confirmed that the issue had been mitigated. This is consistent with our independent audit of the affected platforms. Analysis code and redacted datasets will be made available to those who wish to reproduce our findings.

Android implements a permission system to regulate apps' access to system resources and sensitive user data. One salient feature of this system is its extensibility: apps can define their own custom permissions to expose features and data to other apps. However, little is known about how widespread the usage of custom permissions is, and what is the impact that these permissions can have on users' privacy and security. In this paper, we empirically study the usage of custom permissions at large scale, using a dataset of 2.2M pre-installed and app-store-downloaded apps. We find the usage of custom permissions to be widespread, and seemingly growing over time. Despite this prevalence, we find that custom permissions are virtually invisible to end users, and their purpose mostly undocumented. This lack of transparency can lead to serious security and privacy problems: we show that custom permissions can facilitate access to permission-protected system resources to apps that lack those permissions without user awareness. To detect this practice, we design and implement two static analysis tools, and highlight multiple concerning cases spotted in the wild. We conclude this study with a discussion of potential solutions to mitigate the privacy and security risks of custom permissions.

The transparency and privacy behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one end, they can have access to (and can expose) a unique combination of sensitive user data, from users’ browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. However, on the other end, they also are in a unique position to protect users’ privacy by limiting data sharing with other parties by implementing ad-blocking features. In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we developed a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions. Overall, our study not only provides new insights into important yet overlooked considerations for browsers’ adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need context-specific analysis to reveal such privacy behaviors.

The ability to identify the author responsible for a given software object is critical for many research studies and for enhancing software transparency and accountability. However, as opposed to other application markets like Apple's iOS App Store, attribution in the Android ecosystem is known to be hard. Prior research has leveraged market metadata and signing certificates to identify software authors without questioning the validity and accuracy of these attribution signals. However, Android application (app) authors can, either intentionally or by mistake, hide their true identity due to: (1) the lack of policy enforcement by markets to ensure the accuracy and correctness of the information disclosed by developers in their market profiles during the app release process, and (2) the use of self-signed certificates for signing apps instead of certificates issued by trusted CAs. In this paper, we perform the first empirical analysis of the availability, volatility and overall aptness of publicly available market and app metadata for author attribution in Android markets. To that end, we analyze a dataset of over 2.5 million market entries and apps extracted from five Android markets for over two years. Our results show that widely used attribution signals are often missing from market profiles and that they change over time. We also invalidate the general belief about the validity of signing certificates for author attribution. For instance, we find that apps from different authors share signing certificates due to the proliferation of app building frameworks and software factories. Finally, we introduce the concept of anattribution graphand we apply it to evaluate the validity of existing attribution signals on the Google Play Store. Our results confirm that the lack of control over publicly available signals can confuse automatic attribution processes.

Android firmware updates are typically managed by the so-called FOTA (Firmware Over-the-Air) apps. Such apps are highly privileged and play a critical role in maintaining devices secured and updated. The Android operating system offers standard mechanisms—available to Original Equipment Manufacturers (OEMs)—to implement their own FOTA apps but such vendor-specific implementations could be a source of security and privacy issues due to poor software engineering practices. This paper performs the first large-scale and systematic analysis of the FOTA ecosystem through a dataset of 2,013 FOTA apps detected with a tool designed for this purpose over 422,121 pre-installed apps. We classify the different stakeholders developing and deploying FOTA apps on the Android update ecosystem, showing that 43% of FOTA apps are developed by third parties. We report that some devices can have as many as 5 apps implementing FOTA capabilities. By means of static analysis of the code of FOTA apps, we show that some apps present behaviors that can be considered privacy intrusive, such as the collection of sensitive user data (e.g., geolocation linked to unique hardware identifiers), and a significant presence of third-party trackers. We also discover implementation issues leading to critical vulnerabilities, such as the use of public AOSP test keys both for signing FOTA apps and for update verification, thus allowing any update signed with the same key to be installed. Finally, we study telemetry data collected from real devices by a commercial security tool. We demonstrate that FOTA apps are responsible for the installation of non-system apps (e.g., entertainment apps and games), including malware and Potentially Unwanted Programs (PUP). Our findings suggest that FOTA development practices are misaligned with Google’s recommendations.

Blocklists constitute a widely-used Internet security mechanism to filter undesired network traffic based on IP/domain reputation and behavior. Many blocklists are distributed in open source form by threat intelligence providers who aggregate and process input from their own sensors, but also from third-party feeds or providers. Despite their wide adoption, many open-source blocklist providers lack clear documentation about their structure, curation process, contents, dynamics, and inter-relationships with other providers. In this paper, we perform a transparency and content analysis of 2,093 free and open source blocklists with the aim of exploring those questions. To that end, we perform a longitudinal 6-month crawling campaign yielding more than 13.5M unique records. This allows us to shed light on their nature, dynamics, inter-provider relationships, and transparency. Specifically, we discuss how the lack of consensus on distribution formats, blocklist labeling taxonomy, content focus, and temporal dynamics creates a complex ecosystem that complicates their combined crawling, aggregation and use. We also provide observations regarding their generally low overlap as well as acute differences in terms of liveness (i.e., how frequently records get indexed and removed from the list) and the lack of documentation about their data collection processes, nature and intended purpose. We conclude the paper with recommendations in terms of transparency, accountability, and standardization.

"Incentivized" advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps (e.g., create an account, make in-app purchases). Incentivized installs are banned by the Apple App Store and discouraged by the Google Play Store because they can manipulate app store metrics (e.g., install counts, appearance in top charts). Yet, many organizations still offer incentivized install services for Android apps. In this paper, we present the first study to understand the ecosystem of incentivized mobile app install campaigns in Android and its broader ramifications through a series of measurements. We identify incentivized install campaigns that require users to install an app and perform in-app tasks targeting manipulation of a wide variety of user engagement metrics (e.g., daily active users, user session lengths) and revenue. Our results suggest that these artificially inflated metrics can be effective in improving app store metrics as well as helping mobile app developers to attract funding from venture capitalists. Our study also indicates lax enforcement of the Google Play Store's existing policies to prevent these behaviors. It further motivates the need for stricter policing of incentivized install campaigns. Our proposed measurements can also be leveraged by the Google Play Store to identify potential policy violations.

Domain classification services have applications in multiple areas, including cybersecurity, content blocking, and targeted advertising. Yet, these services are often a black box in terms of their methodology to classifying domains, which makes it difficult to assess their strengths, aptness for specific applications, and limitations. In this work, we perform a large-scale analysis of 13 popular domain classification services on more than 4.4M hostnames. Our study empirically explores their methodologies, scalability limitations, label constellations, and their suitability to academic research as well as other practical applications such as content filtering. We find that the coverage varies enormously across providers, ranging from over 90% to below 1%. All services deviate from their documented taxonomy, hampering sound usage for research. Further, labels are highly inconsistent across providers, who show little agreement over domains, making it difficult to compare or combine these services. We also show how the dynamics of crowd-sourced efforts may be obstructed by scalability and coverage aspects as well as subjective disagreements among human labelers. Finally, through case studies, we showcase that most services are not fit for detecting specialized content for research or content-blocking purposes. We conclude with actionable recommendations on their usage based on our empirical insights and experience. Particularly, we focus on how users should handle the significant disparities observed across services both in technical solutions and in research.

Abstract Android parental control applications are used by parents to monitor and limit their children’s mobile behaviour (e.g., mobile apps usage, web browsing, calling, and texting). In order to offer this service, parental control apps require privileged access to system resources and access to sensitive data. This may significantly reduce the dangers associated with kids’ online activities, but it raises important privacy concerns. These concerns have so far been overlooked by organizations providing recommendations regarding the use of parental control applications to the public. We conduct the first in-depth study of the Android parental control app’s ecosystem from a privacy and regulatory point of view. We exhaustively study 46 apps from 43 developers which have a combined 20M installs in the Google Play Store. Using a combination of static and dynamic analysis we find that: these apps are on average more permissions-hungry than the top 150 apps in the Google Play Store, and tend to request more dangerous permissions with new releases; 11% of the apps transmit personal data in the clear; 34% of the apps gather and send personal information without appropriate consent; and 72% of the apps share data with third parties (including online advertising and analytics services) without mentioning their presence in their privacy policies. In summary, parental control applications lack transparency and lack compliance with regulatory requirements. This holds even for those applications recommended by European and other national security centers.

Abstract It is commonly assumed that “free” mobile apps come at the cost of consumer privacy and that paying for apps could offer consumers protection from behavioral advertising and long-term tracking. This work empirically evaluates the validity of this assumption by comparing the privacy practices of free apps and their paid premium versions, while also gauging consumer expectations surrounding free and paid apps. We use both static and dynamic analysis to examine 5,877 pairs of free Android apps and their paid counterparts for differences in data collection practices and privacy policies between pairs. To understand user expectations for paid apps, we conducted a 998-participant online survey and found that consumers expect paid apps to have better security and privacy behaviors. However, there is no clear evidence that paying for an app will actually guarantee protection from extensive data collection in practice. Given that the free version had at least one thirdparty library or dangerous permission, respectively, we discovered that 45% of the paid versions reused all of the same third-party libraries as their free versions, and 74% of the paid versions had all of the dangerous permissions held by the free app. Likewise, our dynamic analysis revealed that 32% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Finally, we found that 40% of apps did not have a privacy policy link in the Google Play Store and that only 3.7% of the pairs that did reflected differences between the free and paid versions.

Modern privacy regulations, including the General Data Protection Regulation (GDPR) in the European Union, aim to control user tracking activities in websites and mobile applications. These privacy rules typically contain specific provisions and strict requirements for websites that provide sensitive material to end users such as sexual, religious, and health services. However, little is known about the privacy risks that users face when visiting such websites, and about their regulatory compliance. In this paper, we present the first comprehensive and large-scale analysis of 6,843 pornographic websites. We provide an exhaustive behavioral analysis of the use of tracking methods by these websites, and their lack of regulatory compliance, including the absence of age-verification mechanisms and methods to obtain informed user consent. The results indicate that, as in the regular web, tracking is prevalent across pornographic sites: 72% of the websites use third-party cookies and 5% leverage advanced user fingerprinting technologies. Yet, our analysis reveals a third-party tracking ecosystem semi-decoupled from the regular web in which various analytics and advertising services track users across, and outside, pornographic websites. We complete the paper with a regulatory compliance analysis in the context of the EU GDPR, and newer legal requirements to implement verifiable access control mechanisms (e.g., UK's Digital Economy Act). We find that only 16% of the analyzed websites have an accessible privacy policy and only 4% provide a cookie consent banner. The use of verifiable access control mechanisms is limited to prominent pornographic websites.