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Mobile apps are used in a variety of health settings, from apps that help providers, to apps designed for patients, to health and fitness apps designed for the general public. These apps ask the user for, and then collect and leak a wealth…
Mobile phones have developed into complex platforms with large numbers of installed applications and a wide range of sensitive data. Application security policies limit the permissions of each installed application. As applications may…
The massive growth of the Internet of Things (IoT) as a network of interconnected entities [18], brings up new challenges in terms of privacy and security requirements to the traditional software engineering domain [4]. To protect the…
Differential privacy (DP) has become the de facto standard for protecting sensitive data, providing strong guarantees that published statistics or models reveal limited information about any individual. However, privacy noise and restricted…
Natural language processing (NLP) models may leak private information in different ways, including membership inference, reconstruction or attribute inference attacks. Sensitive information may not be explicit in the text, but hidden in…
A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the…
Differential privacy (DP) has arisen as the state-of-the-art metric for quantifying individual privacy when sensitive data are analyzed, and it is starting to see practical deployment in organizations such as the US Census Bureau, Apple,…
The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart…
Due to the amount of data that smartphone applications can potentially access, platforms enforce permission systems that allow users to regulate how applications access protected resources. If users are asked to make security decisions too…
Smartphone users install numerous mobile apps that require access to different information from their devices. Much of this information is very sensitive, and users often struggle to manage these accesses due to their lack of tech expertise…
With the growing amount of personal information exchanged over the Internet, privacy is becoming more and more a concern for users. One of the key principles in protecting privacy is data minimisation. This principle requires that only the…
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…
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…
Streaming data collection is essential to real-time data analytics in various IoTs and mobile device-based systems, which, however, may expose end users' privacy. Local differential privacy (LDP) is a promising solution to…
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.…
With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of…
Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising…
Context: As mobile applications (Apps) widely spread over our society and life, various personal information is constantly demanded by Apps in exchange for more intelligent and customized functionality. An increasing number of users are…
The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a result, many independent efforts have emerged that try to provide…
Differential privacy (DP) is a mathematical definition of privacy that can be widely applied when publishing data. DP has been recognized as a potential means of adhering to various privacy-related legal requirements. However, it can be…