Security and Privacy Enhancing Multi-Cloud Architectures

Security challenges are still among the biggest obstacles when considering the adoption of cloud services. This triggered a lot of research activities, resulting in a quantity of proposals targeting the various cloud security threats. Alongside with these security issues, the cloud paradigm comes with a new set of unique features, which open the path toward novel security approaches, techniques, and architectures. This paper provides a survey on the achievable security merits by making use of multiple distinct clouds simultaneously. Various distinct architectures are introduced and discussed according to their security and privacy capabilities and prospects

Privacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning

The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts.</p

Challenges in data science: a complex systems perspective

The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by "Big Data". Deep insight is required for understanding interactions among connected systems, space- and time-dependent heterogeneous data structures. Emergence of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: "What can Complex Systems Science contribute to Big Data? ". Such question can be reversed and brought to a superior level of abstraction by asking "What Knowledge can be drawn from Big Data?" These aspects constitute the main motivation behind this article to introduce a volume containing a collection of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physic