Related papers: Privacy Engineering Meets Software Engineering. On…
Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research conducted in a…
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. While these challenges have been widely discussed in previous…
Modern privacy regulations, such as the General Data Protection Regulation (GDPR), address privacy in software systems in a technologically agnostic way by mentioning general "technical measures" for data privacy compliance rather than…
Security engineering in the software lifecycle aims at protecting information and systems to guarantee confidentiality, integrity, and availability. As security engineering matures and the number of research papers grows, there is an…
Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems…
The last decade has seen a rise of Deep Learning with its applications ranging across diverse domains. But usually, the datasets used to drive these systems contain data which is highly confidential and sensitive. Though, Deep Learning…
Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy…
Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners.…
Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data. In this context, the promise of formal privacy…
Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning…
User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these…
The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around…
Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data…
Autonomous systems are emerging in many application domains. With the recent advancements in artificial intelligence and machine learning, sensor technology, perception algorithms and robotics, scenarios previously requiring strong human…
Privacy by design will become a legal obligation in the European Community if the Data Protection Regulation eventually gets adopted. However, taking into account privacy requirements in the design of a system is a challenging task. We…
Analyzing privacy threats in software products is an essential part of software development to ensure systems are privacy-respecting; yet it is still a far from trivial activity. While there have been many advancements in the past decade,…
Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and…
Recent progress in artificial intelligence (AI) using deep learning techniques has triggered its wide-scale use across a broad range of applications. These systems can already perform tasks such as natural language processing of voice and…
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applications. More and more program functions are no longer written in code, but learnt from a huge amount of data samples using an ML algorithm.…
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…