相关论文: AndroByte: LLM-Driven Privacy Analysis through Byt…

Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review (SLR) examines…

Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since in 2018 the European Union rolled out the General Data Protection…

In recent years, there has been rapid growth in mobile devices such as smartphones, and a number of applications are developed specifically for the smartphone market. In particular, there are many applications that are ``free'' to the user,…

Many Android applications collect data from users. When they do, they must protect this collected data according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the…

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end user devices at the cost of recording and transmitting logs…

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering…

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the processing of personal and sensitive data, like that no such…

The increasing use of machine learning (ML) for Just-In-Time (JIT) defect prediction raises concerns about privacy leakage from software analytics data. Existing anonymization methods, such as tabular transformations and graph…

Mobile application security has been a major area of focus for security research over the course of the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However,…

With mobile applications and associated services becoming increasingly popular, concerns are being raised about private data leakages have raised. Previous solutions to this well-known set of problems have approached it from the ground up…

Mobile Large Language Models (LLMs) are revolutionizing diverse fields such as healthcare, finance, and education with their ability to perform advanced natural language processing tasks on-the-go. However, the deployment of these models in…

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a…

The rapid evolution of Large Language Models (LLMs) has unlocked new possibilities for applying artificial intelligence across a wide range of fields, including privacy engineering. As modern applications increasingly handle sensitive user…

Privacy policies serve as the primary conduit through which online service providers inform users about their data collection and usage procedures. However, in a bid to be comprehensive and mitigate legal risks, these policy documents are…

Contemporary mobile applications (apps) are designed to track, use, and share users' data, often without their consent, which results in potential privacy and transparency issues. To investigate whether mobile apps have always been…

Mobile apps have become essential of our daily lives, making code quality a critical concern for developers. Behavioural code smells are characteristics in the source code that induce inappropriate code behaviour during execution, which…

With the rapid growth of mobile apps, users' concerns about their privacy have become increasingly prominent. Android app logs serve as crucial computer resources, aiding developers in debugging and monitoring the status of Android apps,…

Access to privacy-sensitive information on Android is a growing concern in the mobile community. Albeit Google Play recently introduced some privacy guidelines, it is still an open problem to soundly verify whether apps actually comply with…

This research addresses privacy protection in Natural Language Processing (NLP) by introducing a novel algorithm based on differential privacy, aimed at safeguarding user data in common applications such as chatbots, sentiment analysis, and…

Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imbalanced datasets. Synthetic data has been proposed as a…