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1. Securing IoT Apps with Fine-grained Control of Information Flows

   Mauro Junior, Davino; Gama, Kiev; Prakash, Atul (January 2018, XVIII Brazilian Symposium On Information and Computational Systems Security)

   Internet of Things is growing rapidly, with many connected devices now available to consumers. With this growth, the IoT apps that manage the devices from smartphones raise significant security concerns. Typically, these apps are secured via sensitive credentials such as email and password that need to be validated through specific servers, thus requiring permissions to access the Internet. Unfortunately, even when developers of these apps are well-intentioned, such apps can be non-trivial to secure so as to guarantee that user’s credentials do not leak to unauthorized servers on the Internet. For example, if the app relies on third-party libraries, as many do, those libraries can potentially capture and leak sensitive credentials. Bugs in the applications can also result in exploitable vulnerabilities that leak credentials. This paper presents our work in-progress on a prototype that enables developers to control how information flows within the app from sensitive UI data to specific servers. We extend FlowFence to enforce fine-grained information flow policies on sensitive UI data. A version of the paper is also available at: https://arxiv.org/abs/1810.13367. The final version is available at: https://portaldeconteudo.sbc.org.br/index.php/sbseg/article/view/4263 

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2. DAISY: Dynamic-Analysis-Induced Source Discovery for Sensitive Data

   https://doi.org/10.1145/3569936  

   Zhang, Xueling; Heaps, John; Slavin, Rocky; Niu, Jianwei; Breaux, Travis D.; Wang, Xiaoyin (October 2022, ACM Transactions on Software Engineering and Methodology)

   Mobile apps are widely used and often process users’ sensitive data. Many taint analysis tools have been applied to analyze sensitive information flows and report data leaks in apps. These tools require a list of sources (where sensitive data is accessed) as input, and researchers have constructed such lists within the Android platform by identifying Android API methods that allow access to sensitive data. However, app developers may also define methods or use third-party library’s methods for accessing data. It is difficult to collect such source methods because they are unique to the apps, and there are a large number of third-party libraries available on the market that evolve over time. To address this problem, we propose DAISY, a Dynamic-Analysis-Induced Source discoverY approach for identifying methods that return sensitive information from apps and third-party libraries. Trained on an automatically labeled data set of methods and their calling context, DAISY identifies sensitive methods in unseen apps. We evaluated DAISY on real-world apps and the results show that DAISY can achieve an overall precision of 77.9% when reporting the most confident results. Most of the identified sources and leaks cannot be detected by existing technologies. 

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3. IconIntent: Automatic Identification of Sensitive UI Widgets based on Icon Classification for Android Apps

   Xiao, Xusheng; Wang, Xiaoyin; Cao, Zhihao; Wang, Hanlin; Gao, Peng (January 2019, Proceedings - International Conference on Software Engineering)

   Many mobile applications (i.e., apps) include UI widgets to use or collect users’ sensitive data. Thus, to identify suspicious sensitive data usage such as UI-permission mis- match, it is crucial to understand the intentions of UI widgets. However, many UI widgets leverage icons of specific shapes (object icons) and icons embedded with text (text icons) to express their intentions, posing challenges for existing detection techniques that analyze only textual data to identify sensitive UI widgets. In this work, we propose a novel app analysis frame- work, ICONINTENT, that synergistically combines program analysis and icon classification to identify sensitive UI widgets in Android apps. ICONINTENT automatically associates UI widgets and icons via static analysis on app’s UI layout files and code, and then adapts computer vision techniques to classify the associated icons into eight categories of sensitive data. Our evaluations of ICONINTENT on 150 apps from Google Play show that ICONINTENT can detect 248 sensitive UI widgets in 97 apps, achieving a precision of 82.4%. When combined with SUPOR, the state-of-the-art sensitive UI widget identification technique based on text analysis, SUPOR +ICONINTENT can detect 487 sensitive UI widgets (101.2% improvement over SU- POR only), and reduces suspicious permissions to be inspected by 50.7% (129.4% improvement over SUPOR only). 

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4. A STUDY OF PERSONAL INFORMATION LEAKS IN MOBILE MEDICAL, HEALTH, AND FITNESS APPS

   Ardalani, Alireza; Antonucci, Joseph; Neamtiu, Iulian (December 2024, IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET)

   There has been a proliferation of mobile apps in the Medical, as well as Health&Fitness categories. These apps have a wide audience, from medical providers, to patients, to end users who want to track their fitness goals. The low barrier to entry on mobile app stores raises questions about the diligence and competence of the developers who publish these apps, especially regarding the practices they use for user data collection, processing, and storage. To help understand the nature of data that is collected, and how it is processed, as well as where it is sent, we developed a tool named PIT (Personal Information Tracker) and made it available as open source. We used PIT to perform a multi-faceted study on 2832 Android apps: 2211 Medical apps and 621 Health&Fitness apps. We first define Personal Information (PI) as 17 different groups of sensitive information, e.g., user’s identity, address and financial information, medical history or anthropometric data. PIT first extracts the elements in the app’s User Interface (UI) where this information is collected. The collected information could be processed by the app’s own code or third-party code; our approach disambiguates between the two. Next, PIT tracks, via static analysis, where the information is “leaked”, i.e., it escapes the scope of the app, either locally on the phone or remotely via the network. Then, we conduct a link analysis that examines the URLs an app connects with, to understand the origin and destination of data that apps collect and process. We found that most apps leak 1–5 PI items (email, credit card, phone number, address, name, being the most frequent). Leak destinations include the network (25%), local databases (37%), logs (23%), and files or I/O (15%). While Medical apps have more leaks overall, as they collect data on medical history, surprisingly, Health&Fitness apps also collect, and leak, medical data. We also found that leaks that are due to third-party code (e.g., code for ads, analytics, or user engagement) are much more numerous (2x–12x) than leaks due to app’s own code. Finally, our link analysis shows that most apps access 20–80 URLs (typically third-party URLs and Cloud APIs) though some apps could access more than 1,000 URLs. 

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5. A Survey of Patterns for Adapting Smartphone App UIs to SmartWatches

   https://doi.org/10.1145/3379503.3403564  

   Zhou, Zhilan; Xu, Jian; Balasubramanian, Aruna; Porter, Donald E. (January 2020, 22nd International Conference on Human-Computer Interaction with MobileDevices and Services (MobileHCI ’20),)

   Wearable devices, such as smart watches and fitness trackers are growing in popularity, creating a need for application developers to adapt or extend a UI, typically from a smartphone, onto these devices. Wearables generally have a smaller form factor than a phone; thus, porting an app to the watch necessarily involves reworking the UI. An open problem is identifying best practices for adapting UIs to wearable devices. This paper contributes a study and data set of the state of practice in UI adaptation for wearables. We automatically extract UI designs from a set of 101 popular Android apps that have both a phone and watch version, and manually label how each UI element, as well as how screens in the app, are translated from the phone to the wearable. The paper identifies trends in adaptation strategies and presents design guidelines. We expect that the UI adaptation strategies identified in this paper can have wide-ranging impacts for future research and identifying best practices in this space, such as grounding future user studies that evaluate which strategies improve user satisfaction or automatically adapting UIs. 

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