相关论文: Motivating Users to Attend to Privacy: A Theory-Dr…

As technology and technology companies have grown in power, ubiquity, and societal influence, some companies -- and notably some mobile apps -- have come to be perceived as privacy threats. Prior work has considered how various factors…

The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a…

Using Privacy-Enhancing Technologies (PETs) for machine learning often influences the characteristics of a machine learning approach, e.g., the needed computational power, timing of the answers or how the data can be utilized. When…

In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in…

Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly…

In today's mobile application marketplace, the ability of consumers to make informed choices regarding their privacy is extremely limited. Consumers largely rely on privacy policies and app permission mechanisms, but these do an inadequate…

The mobile device is one of the fasted growing technologies that is widely used in a diversifying sector. Mobile devices are used for everyday life, such as personal information exchange - chatting, email, shopping, and mobile banking,…

Nowadays, mobile users have a vast number of applications and services at their disposal. Each of these might impose some privacy threats on users' "Personally Identifiable Information" (PII). Location privacy is a crucial part of PII, and…

To account for privacy perceptions and preferences in user models and develop personalized privacy systems, we need to understand how users make privacy decisions in various contexts. Existing studies of privacy perceptions and behavior…

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by…

In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks…

Recent years have seen a surge in applications and technologies aimed at motivating users to achieve personal goals and improve their wellbeing. However, these often fail to promote long-term behaviour change, and sometimes even backfire.…

To create privacy-friendly software designs, architects need comprehensive knowledge of existing privacy-enhancing technologies (PETs) and their properties. Existing works that systemize PETs, however, are outdated or focus on comparison…

Existing work on privacy by design mostly focus on technologies rather than methodologies and on components rather than architectures. In this paper, we advocate the idea that privacy by design should also be addressed at the architectural…

The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also raises serious privacy concerns, especially in light of…

Designing Internet of things (IoT) applications (apps) is challenging due to the heterogeneous nature of the systems on which these apps are deployed. Personal data, often classified as sensitive, may be collected and analysed by IoT apps,…

Privacy policies are intended to support informed consent, yet users rarely read them fully. This study examines how common privacy policy interface structures influence attention allocation, reading behavior, and perceived experience.…

Adolescents heavily rely on social media to build and maintain close relationships, yet current platform designs often make self-disclosure feel risky or uncomfortable. Through a three-part study involving 19 teens aged 13-18, we identify…

Large language models (LLMs) are increasingly used to simulate human behavior, but their ability to simulate $individual$ privacy decisions is not well understood. In this paper, we address the problem of evaluating whether a core set of…

In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first derive a model to measure the perceived privacy risk based on…