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Abstract-Information practices and systems that make use of personal and health-related information are governed by European laws and regulations to prevent unauthorized use and disclosure. Failure to comply with these laws and regulations results in huge monetary sanctions, which both private companies and public administrations want to avoid. How to comply with these laws, requires understanding the privacy requirements imposed on information systems. A holistic approach to privacy requirements specification calls for understanding not only the requirements derived from law, but also citizens' needs with respect to privacy. In this paper, we report on our experience in conducting privacy requirements engineering as part of a H2020 European Project, namely VisiOn (Visual Privacy Management in User Centric Open Requirements) for the development of a privacy platform to improve the interaction between Public Administrations (PA) and citizens, while guarding the privacy of the latter. Specifically, we present the process for eliciting, classifying, prioritizing, and validating privacy requirements for the two types of users, namely PA and citizen. The process is applied to different cases spanning from healthcare to other e-governmental initiatives, with the active involvement of the corresponding PAs. We report on findings and lessons learned from this experience.

Many business processes (BPs) involving critical decision-making activities require good quality information for their successful enactment. Despite this fact, existing BP approaches focus on control-flow and ignore the complementary information perspective, or simply treat it as a technical issue, rather than a social and organizational one. To tackle this problem, we propose a comprehensive framework for modeling and analyzing information quality requirements for business processes using the WFA-net BP modeling language. In addition, we describe a prototype implementation, and present two realistic examples concerning the stock market domain, intended to illustrate our approach.

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Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.