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The European General Data Protection Regulation requires the implementation of Technical and Organizational Measures (TOMs) to reduce the risk of illegitimate processing of personal data. For these measures to be effective, they must be applied correctly by employees who process personal data under the authority of their organization. However, even data processing employees often have limited knowledge of data protection policies and regulations, which increases the likelihood of misconduct and privacy breaches. To lower the likelihood of unintentional privacy breaches, TOMs must be developed with employees’ needs, capabilities, and usability requirements in mind. To reduce implementation costs and help organizations and IT engineers with the implementation, privacy patterns have proven to be effective for this purpose. In this chapter, we introduce the privacy pattern Data Cart, which specifically helps to develop TOMs for data processing employees. Based on a user-centered design approach with employees from two public organizations in Germany, we present a concept that illustrates how Privacy by Design can be effectively implemented. Organizations, IT engineers, and researchers will gain insight on how to improve the usability of privacy-compliant tools for managing personal data.
Users should always play a central role in the development of (software) solutions. The human-centered design (HCD) process in the ISO 9241-210 standard proposes a procedure for systematically involving users. However, due to its abstraction level, the HCD process provides little guidance for how it should be implemented in practice. In this chapter, we propose three concrete practical methods that enable the reader to develop usable security and privacy (USP) solutions using the HCD process. This chapter equips the reader with the procedural knowledge and recommendations to: (1) derive mental models with regard to security and privacy, (2) analyze USP needs and privacy-related requirements, and (3) collect user characteristics on privacy and structure them by user group profiles and into privacy personas. Together, these approaches help to design measures for a user-friendly implementation of security and privacy measures based on a firm understanding of the key stakeholders.
Social policy research on the ageing workforce from the perspective of employees and employers
(2024)
The Peren Theorem
(2024)
The Peren-Clement Index
(2024)
Network Analysis Method
(2024)
Dynamic Programming
(2024)
Heuristic Methods
(2024)
Sequencing Problems
(2024)
Queueing Theory
(2024)
Linear Optimization
(2024)
The Decision Tree Procedure
(2024)
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
Based on the WEF Travel & Tourism Report data, this study deploys k-means cluster analysis to build a global typology of national destination governance. Previous studies have focused on case studies, while this chapter focuses on classification of different destination types, by deploying indicators a set of following relevant indicators: wastewater treatment, fixed broadband internet subscriptions, ground transport efficiency, quality of roads, quality of railroad infrastructure, reliability of police services, ease of finding skilled employees. The results present a four-cluster solution of national destination governance types, as well as their major characteristics. The chapter than provides and discusses important implication for theory and practice of destination governance.
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
From Conclusion to Coda
(2022)
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
(2022)
Virtual exchange
(2022)
Orešković and Porsdam Mann draw a distinction between ‘fast’ and ‘slow’ science. Whereas the latter involves rigorous and laborious adherence to the scientific method, the former represents the reality that much scientific work faces time pressures which at times force shortcuts. The distinction can be seen to operate in contemporary research into the coronavirus pandemic: whereas the development of vaccines and treatments usually requires years of meticulous laboratory work and several more years of clinical testing, the many millions suffering from the disease need a treatment now. However, by taking too many safeguards off the treatment discovery and testing pipelines, or by refusing to act in accordance with scientific advice, governments risk sacrificing the public’s trust not only in the government’s scientific bona fides but in the scientific process itself. This is a heavy price to pay, argue Orešković and Porsdam Mann, and point to evidence indicating that the success of Germany and Japan in combating COVID-19 can be traced to public trust in science and government, as well as scientifically-informed and respectful national leadership.