相关论文: Catering to Your Concerns: Automatic Generation of…

People candidly discuss sensitive topics online under the perceived safety of anonymity; yet, for many, this perceived safety is tenuous, as miscalibrated risk perceptions can lead to over-disclosure. Recent advances in Natural Language…

We present the Android app TYDR (Track Your Daily Routine) which tracks smartphone sensor and usage data and utilizes standardized psychometric personality questionnaires. With the app, we aim at collecting data for researching correlations…

With the growing global emphasis on regulating the protection of personal information and increasing user expectation of the same, developing with privacy in mind is becoming ever more important. In this paper, we study the concerns,…

With the Increasing use of Machine Learning in Android applications, more research and efforts are being put into developing better-performing machine learning algorithms with a vast amount of data. Along with machine learning for mobile…

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…

While extremely valuable to achieve advanced functions, mobile phone sensors can be abused by attackers to implement malicious activities in Android apps, as experimentally demonstrated by many state-of-the-art studies. There is hence a…

As mobile app usage continues to rise, so does the generation of extensive user interaction data, which includes actions such as swiping, zooming, or the time spent on a screen. Apps often collect a large amount of this data and claim to…

Many software systems, such as online social networks enable users to share information about themselves. While the action of sharing is simple, it requires an elaborate thought process on privacy: what to share, with whom to share, and for…

High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated data will soon have been used. Additionally, publicly…

While the literature on permissions from the end-user perspective is rich, there is a lack of empirical research on why developers request permissions, their conceptualization of permissions, and how their perspectives compare with…

Personal devices (e.g. laptops, tablets, and mobile phones) are conventional in daily life and have the ability to store users' private data. The security problems related to these appliances have become a primary concern for both users and…

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more…

App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for…

With the digital breakthrough, smart phones have become very essential component. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential…

UX (user experience) designers visually capture the UX of an app via storyboards. This method is also used in Android app development to conceptualize and design apps. Recently, security has become an integral part of Android app UX because…

Enabling fully automated testing of mobile applications has recently become an important topic of study for both researchers and practitioners. A plethora of tools and approaches have been proposed to aid mobile developers both by…

The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To…

Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which recommendation outputs may negatively impact users by violating…

In the landscape of application ecosystems, today's cloud users wish to personalize not only their browsers with various extensions or their smartphones with various applications, but also the various extensions and applications themselves.…

The problem of writing a specification which accurately reflects the intent of the developer has long been recognized as fundamental. We propose a method and a supporting tool to write and check a specification and an implementation using a…