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1. Do You Get What You Pay For? Comparing The Privacy Behaviors of Free vs. Paid Apps

   Han, Catherine; Reyes, Irwin; Elazari Bar On, Amit; Reardon, Joel; Feal, Álvaro; Bamberger, Kenneth A.; Egelman, Serge; Vallina-Rodriguez, Narseo (June 2019, The Workshop on Technology and Consumer Protection (ConPro ’19))

   It is commonly assumed that the availability of “free” mobile apps comes 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 investigating the degree to which “free” apps and their paid premium versions differ in their bundled code, their declared permissions, and their data collection behaviors and privacy practices. We compare pairs of free and paid apps using a combination of static and dynamic analysis. We also examine the differences in the privacy policies within pairs. We rely on static analysis to determine the requested permissions and third-party SDKs in each app; we use dynamic analysis to detect sensitive data collected by remote services at the network traffic level; and we compare text versions of privacy policies to identify differences in the disclosure of data collection behaviors. In total, we analyzed 1,505 pairs of free Android apps and their paid counterparts, with free apps randomly drawn from the Google Play Store’s category-level top charts. Our results show that over our corpus of free and paid pairs, there is no clear evidence that paying for an app will guarantee protection from extensive data collection. Specifically, 48% of the paid versions reused all of the same third-party libraries as their free versions, while 56% of the paid versions inherited all of the free versions’ Android permissions to access sensitive device resources (when considering free apps that include at least one third-party library and request at least one Android permission). Additionally, our dynamic analysis reveals that 38% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Our exploration of privacy policies reveals that only 45% of the pairs provide a privacy policy of some sort, and less than 1% of the pairs overall have policies that differ between free and paid versions. 

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2. Permission vs. App Limiters: Profiling Smartphone Users to Understand Differing Strategies for Mobile Privacy Management

   https://doi.org/10.1145/3491102.3517652  

   Alsoubai, Ashwaq; Ghaiumy Anaraky, Reza; Li, Yao; Page, Xinru; Knijnenburg, Bart; Wisniewski, Pamela J. (April 2022, The 2022 ACM Conference on Human Factors in Computing Systems)

   We conducted a user study with 380 Android users, profiling them according to two key privacy behaviors: the number of apps installed, and the Dangerous permissions granted to those apps. We identified four unique privacy profiles: 1) Privacy Balancers (49.74% of participants), 2) Permission Limiters (28.68%), 3) App Limiters (14.74%), and 4) the Privacy Unconcerned (6.84%). App and Permission Limiters were significantly more concerned about perceived surveillance than Privacy Balancers and the Privacy Unconcerned. App Limiters had the lowest number of apps installed on their devices with the lowest intention of using apps and sharing information with them, compared to Permission Limiters who had the highest number of apps installed and reported higher intention to share information with apps. The four profiles reflect the differing privacy management strategies, perceptions, and intentions of Android users that go beyond the binary decision to share or withhold information via mobile apps. 

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3. PolicyChecker: Analyzing the GDPR Completeness of Mobile Apps' Privacy Policies

   https://doi.org/10.1145/3576915.3623067  

   Xiang, Anhao; Pei, Weiping; Yue, Chuan (November 2023, Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS))

   The European General Data Protection Regulation (GDPR) mandates a data controller (e.g., an app developer) to provide all information specified in Articles (Arts.) 13 and 14 to data subjects (e.g., app users) regarding how their data are being processed and what are their rights. While some studies have started to detect the fulfillment of GDPR requirements in a privacy policy, their exploration only focused on a subset of mandatory GDPR requirements. In this paper, our goal is to explore the state of GDPR-completeness violations in mobile apps' privacy policies. To achieve our goal, we design the PolicyChecker framework by taking a rule and semantic role based approach. PolicyChecker automatically detects completeness violations in privacy policies based not only on all mandatory GDPR requirements but also on all if-applicable GDPR requirements that will become mandatory under specific conditions. Using PolicyChecker, we conduct the first large-scale GDPR-completeness violation study on 205,973 privacy policies of Android apps in the UK Google Play store. PolicyChecker identified 163,068 (79.2%) privacy policies containing data collection statements; therefore, such policies are regulated by GDPR requirements. However, the majority (99.3%) of them failed to achieve the GDPR-completeness with at least one unsatisfied requirement; 98.1% of them had at least one unsatisfied mandatory requirement, while 73.0% of them had at least one unsatisfied if-applicable requirement logic chain. We conjecture that controllers' lack of understanding of some GDPR requirements and their poor practices in composing a privacy policy can be the potential major causes behind the GDPR-completeness violations. We further discuss recommendations for app developers to improve the completeness of their apps' privacy policies to provide a more transparent personal data processing environment to users. 

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4. A Look into User Privacy and Third-party Applications in Facebook

   Seng, Sovantharith; Al-Ameen, Mahdi Nasrullah; Wright, Matthew (July 2021, Information and computer security)

   Furnell, Steven (Ed.)

   A huge amount of personal and sensitive data is shared on Facebook, which makes it a prime target for attackers. Adversaries can exploit third-party applications connected to a user’s Facebook profile (i.e., Facebook apps) to gain access to this personal information. Users’ lack of knowledge and the varying privacy policies of these apps make them further vulnerable to information leakage. However, little has been done to identify mismatches between users’ perceptions and the privacy policies of Facebook apps. We address this challenge in our work. We conducted a lab study with 31 participants, where we received data on how they share information in Facebook, their Facebook-related security and privacy practices, and their perceptions on the privacy aspects of 65 frequently-used Facebook apps in terms of data collection, sharing, and deletion. We then compared participants’ perceptions with the privacy policy of each reported app. Participants also reported their expectations about the types of information that should not be collected or shared by any Facebook app. Our analysis reveals significant mismatches between users’ privacy perceptions and reality (i.e., privacy policies of Facebook apps), where we identified over-optimism not only in users’ perceptions of information collection, but also on their self-efficacy in protecting their information in Facebook despite experiencing negative incidents in the past. To the best of our knowledge, this is the first study on the gap between users’ privacy perceptions around Facebook apps and the reality. The findings from this study offer directions for future research to address that gap through designing usable, effective, and personalized privacy notices to help users to make informed decisions about using Facebook apps. 

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5. "I would not install an app with this label": Privacy Label Impact on Risk Perception and Willingness to Install iOS Apps

   Balas, David G; Ali, Mir Masood; Kanich, Chris; Aviv, Adam J (August 2024, USENIX)

   Starting December 2020, all new and updated iOS apps must display app-based privacy labels. As the first large-scale implementation of privacy nutrition labels in a real-world setting, we aim to understand how these labels affect perceptions of app behavior. Replicating the methodology of Emani-Naeini et al. [IEEE S&P '21] in the space of IoT privacy nutrition labels, we conducted an online study in January 2023 on Prolific with n=1,505 participants to investigate the impact of privacy labels on users' risk perception and willingness to install apps. We found that many privacy label attributes raise participants' risk perception and lower their willingness to install an app. For example, when the app privacy label indicates that financial info will be collected and linked to their identities, participants were 15 times more likely to report increased privacy and security risks associated with the app. Likewise, when a label shows that sensitive info will be collected and used for cross-app/website tracking, participants were 304 times more likely to report a decrease in their willingness to install. However, participants had difficulty understanding privacy label jargon such as diagnostics, identifiers, track and linked. We provide recommendations for enhancing privacy label transparency, the importance of label clarity and accuracy, and how labels can impact consumer choice when suitable alternative apps are available. 

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