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The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them to support societies. This process must be carefully planned, providing guidance that ensures that the data they rely on are dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits. [ABSTRACT FROM AUTHOR]

Addressing privacy and data protection systematically throughout the process of engineering information systems is a daunting task. Although the research community has made significant progress in theory and in labs, meltdowns in recent years suggest that we're still struggling to address systemic privacy issues. Privacy engineering, an emerging field, responds to this gap between research and practice. It's concerned with systematizing and evaluating approaches to capture and address privacy issues with engineering information systems. This article serves to illuminate this nascent field. The authors provide a definition of privacy engineering and describe encompassing activities. They expand on these with findings from the First International Workshop on Privacy Engineering (IWPE), and conclude with future challenges. [ABSTRACT FROM PUBLISHER]

Cloud computing provides well-known economic and technical advantages. However, the absence of knowledge on the privacy capabilities of service providers remains as one of the barriers for the adoption of cloud services. In this paper we describe a mechanism for the quantitative assessment of the privacy practices of different cloud service providers, so that their potential clients can compare among them and choose the one that better meets their requirements. We have validated our contributions in three different scenarios. [ABSTRACT FROM PUBLISHER]

User models generated from personal data are becoming a powerful tool used by service providers to personalize their supply, or introduce new features by means of recommendations or prediction of user behavior, among other applications. However, traditional user modelling requires each service provider to acquire and manage their users' personal information and carry out complex modelling processes. In this paper, we present a comprehensive personal data framework that distributes the user modelling process so that the personal data scattered in heterogeneous sources is used to generate on-demand, dynamic user models that can be retrieved by service providers to meet their business needs. Our proposal has been validated in a financial services scenario. [ABSTRACT FROM PUBLISHER]

Android apps are daily installed by billions of users worldwide, who grant access to an extensive set of sensitive personal data. Different techniques have been developed over the years to understand how apps protect or harm their users' privacy. However, these results have been produced in different research domains and addressing privacy from different perspectives, resulting in a growing but scattered body of knowledge. To bridge this gap, we have carried out a systematic mapping study to provide practitioners and researchers with an overview of the state-of-the-art technique, published between 2016 and 2020, to assess privacy in Android apps. In this paper, we highlight the most relevant findings, identify and analyse the most pressing gaps, and discuss the promising research directions. [ABSTRACT FROM AUTHOR]