Logging is a common practice in software development, widely used for debugging, testing, and performance monitoring. However, recording sensitive user data can introduce severe privacy risks. Past incidents involving leaked logs have prompted platforms such as Android to publish strict guidelines discouraging developers from logging personally identifiable information (PII) and other ''linkable'' or ''ambiguous'' data unless strictly required for core functionality. To evaluate real-world compliance, we examined the logging practices of 500 Android applications across six categories. Our findings reveal that 264 apps contain logging violations, from which we identified 864 instances of sensitive data exposure. Notably, 54% of these violations stem from debugging logs that should have been removed before release. The recorded data includes PII such as email addresses and phone numbers, as well as linkable information such as shopping history, health records, and private messages each in direct violation of Android's privacy guidelines. Moreover, removing these logging statements from application source code does not affect app functionality, raising questions about their necessity. Our analysis shows that most violations originate from debugging practices, third-party analytics tracking, and HTTP request logging. Further, by applying a large language model (LLM) to inspect an additional set of 300 applications, we found 240 apps exhibiting sensitive data logging violations. We also discovered that many apps share log data with third-party services, often contradicting their own privacy policies. To mitigate these risks, we provide practical recommendations for both app developers and mobile platforms to enforce responsible and privacy-preserving logging practices.

Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng, Sohail Dianat, Xiaotian Han, Raghuveer Rao, Xueling Zhang, Cheng Han, Lifu Huang, Dongfang Liu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Applying deep learning to predict patient prognostic survival outcomes using histological whole-slide images (WSIs) and genomic data is challenging due to the morphological and transcriptomic heterogeneity present in the tumor microenvironment. Existing deep learning-enabled methods often exhibit learning biases, primarily because the genomic knowledge used to guide directional feature extraction from WSIs may be irrelevant or incomplete. This results in a suboptimal and sometimes myopic understanding of the overall pathological landscape, potentially overlooking crucial histological insights. To tackle these challenges, we propose the CounterFactual Bidirectional Co-Attention Transformer framework. By integrating a bidirectional co-attention layer, our framework fosters effective feature interactions between the genomic and histology modalities and ensures consistent identification of prognostic features from WSIs. Using counterfactual reasoning, our model utilizes causality to model unimodal and multimodal knowledge for cancer risk stratification. This approach directly addresses and reduces bias, enables the exploration of 'what-if' scenarios, and offers a deeper understanding of how different features influence survival outcomes. Our framework, validated across eight diverse cancer benchmark datasets from The Cancer Genome Atlas (TCGA), represents a major improvement over current histology-genomic model learning methods. It shows an average 2.5% improvement in c-index performance over 18 state-of-the-art models in predicting patient prognoses across eight cancer types.

In this work, we introduce PRoMoTION, a unified proto-typical transformer-based framework engineered to model fundamental motion tasks. PRoMoTION offers a range of compelling attributes that set it apart from current task-specific paradigms. (1) We adopt a prototypical perspective, establishing a unified paradigm that harmonizes disparate motion learning approaches. This novel paradigm stream-lines the architectural design, enabling the simultaneous assimilation of diverse motion information. (2) We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion. This approach effectively circumvents the pitfalls of ambiguity in pixel-wise feature matching, significantly bolstering the robustness of motion representation. (3)) We demon-strate a profound degree of transferability across distinct motion patterns. This inherent versatility reverberates robustly across a comprehensive spectrum of both 2D and 3D downstream tasks. Empirical results demonstrate that PRoMOTION outperforms various well-known specialized architectures, achieving 0.54 and 0.054$AbsRel$error on the Sintel and KITTI depth datasets, 1.04 and 2.01 average endpoint error on the clean and final pass of Sintel flow benchmark, and 4.30 F1-all error on the KITTI flow bench-mark. For its efficacy, we hope our work can catalyze a paradigm shift in universal models in computer vision.

Augmented Reality (AR) technology opens up exciting possibilities in various fields, such as education, work guidance, shopping, communication, and gaming. However, users often encounter usability and user experience issues in current AR apps, often due to the imprecise placement of virtual objects. Detecting these inaccuracies is crucial for AR app testing, but automating the process is challenging due to its reliance on human perception and validation. This paper introduces VOPA (Virtual Object Placement Assessment), a novel approach that automatically identifies imprecise virtual object placements in real-world AR apps. VOPA involves instrumenting real-world AR apps to collect screenshots representing various object placement scenarios and their corresponding metadata under real-world scenes. The collected data are then labeled through crowdsourcing and used to train a hybrid neural network that identifies object placement errors. VOPA aims to enhance AR app testing by automating the assessment of virtual object placement quality and detecting imprecise instances. In our evaluation of a test set of 304 screenshots, VOPA achieved an accuracy of 99.34%, precision of 96.92% and recall of 100%. Furthermore, VOPA successfully identified 38 real-world object placement errors, including instances where objects were hovering between two surfaces or appearing embedded in the wall.

Android phones are among the most popular mobile devices today, providing users with a wide array of convenient services through various apps. These apps generate software logs during their runtime, which record their behavior, status, and error information. However, these logs can also inadvertently capture sensitive information and user privacy data, often without the developer's awareness. In this study, we constructed a dataset comprising 67,702 log records from 83 Android apps. Our analysis of this dataset identified 610 instances of privacy leakage, which indicates the prevalence of such issues in Android app logs. Additionally, our analysis identified characteristics of Android app logs with exposed sensitive information and revealed a gap between developers' awareness of privacy protection and privacy leakage in real-world scenarios.

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 dataset 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.

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement errors of virtual objects. Therefore, identifying noticeable placement errors is an important goal in the testing of AR apps. However, placement errors can only be perceived by human beings and may need to be confirmed by multiple users, making automatic testing very challenging. In this paper, we propose PredART, a novel approach to predict human ratings of virtual object placements that can be used as test oracles in automated AR testing. PredART is based on automatic screenshot sampling, crowd sourcing, and a hybrid neural network for image regression. The evaluation on a test set of 480 screenshots shows that our approach can achieve an accuracy of 85.0% and a mean absolute error, mean squared error, and root mean squared error of 0.047, 0.008, and 0.091, respectively.

With the development of augmented reality (AR) technology, the use of mobile AR applications (MAR apps) is rising rapidly in various aspects of people’s everyday lives, such as games, shopping, and education. When compared to traditional apps, AR apps typically need access to the smartphone’s camera all the time and collect and analyze significantly more data, such as sensor data, geolocation, and biometric information. Due to the sensitivity and volume of data collected by MAR apps, new privacy concerns are raised. In this paper, we describe a preliminary empirical study of Android MAR apps in terms of the sensitive data collected by MAR apps, whether the collected data is well protected, and whether the data practice is publicly available so that users can learn about the data safety and make informed decisions when deciding which apps to install. In this study, we analyzed 390 real-world MAR apps and reported the dangerous permissions they requested, the data leaks detected in them, and the availability of their data safety.

Static taint analyses are widely-applied techniques to detect taint flows in software systems. Although they are theoretically conservative and de-signed to detect all possible taint flows, static taint analyses almost always exhibit false negatives due to a variety of implementation limitations. Dynamic programming language features, inaccessible code, and the usage of multiple programming languages in a software project are some of the major causes. To alleviate this problem, we developed a novel approach, DySTA, which uses dynamic taint analysis results as additional sources for static taint analysis. However, naïvely adding sources causes static analysis to lose context sensitivity and thus produce false positives. Thus, we developed a hybrid context matching algorithm and corresponding tool, ConDySTA, to preserve context sensitivity in DySTA. We applied REPRODROID [1], a comprehensive benchmarking framework for Android analysis tools, to evaluate ConDySTA. The results show that across 28 apps (1) ConDySTA was able to detect 12 out of 28 taint flows which were not detected by any of the six state-of-the-art static taint analyses considered in ReproDroid, and (2) ConDySTA reported no false positives, whereas nine were reported by DySTA alone. We further applied ConDySTA and FlowDroid to 100 top Android apps from Google Play, and ConDySTA was able to detect 39 additional taint flows (besides 281 taint flows found by FlowDroid) while preserving the context sensitivity of FlowDroid.

Mobile application (app) developers commonly utilize analytic services to analyze their app users' behavior to support debugging, improve service quality, and facilitate advertising. Anonymization and aggregation can reduce the sensitivity of such behavioral data, therefore analytic services often encourage the use of such protections. However, these protections are not directly enforced so it is possible for developers to misconfigure the analytic services and expose personal information, which may cause greater privacy risks. Since people use apps in many aspects of their daily lives, such misconfigurations may lead to the leaking of sensitive personal information such as a users' real-time location, health data, or dating preferences. To study this issue and identify potential privacy risks due to such misconfigurations, we developed a semi-automated approach, Privacy-Aware Analytics Misconfiguration Detector (PAMDroid), which enables our empirical study on mis-configurations of analytic services. This paper describes a study of 1,000 popular apps using top analytic services in which we found misconfigurations in 120 apps. In 52 of the 120 apps, misconfigurations lead to a violation of either the analytic service providers' terms of service or the app's own privacy policy.

Augmented Reality (AR) is an emerging technique that enriches real environment with virtual information objects. Despite its wide application scenarios, AR techniques also raise concerns on its dependability, especially on the privacy protection of the users and of the people appearing in users' eyesight. In our research, we performed a case study on the mostly popular augmented reality Android app: Google Translate. In this paper, we report our major findings in the case study, and propose potential mechanism to detect unnecessary privacy leaks in Android augmented reality apps.