Modellbasierte Entscheidungsunterstützung für Vertraulichkeit und Datenschutz in Geschäftsprozessen

Informationsvertraulichkeits- und Datenschutz-Netze (ICPN) unterstützen Prozessmodellierer und -verantwortliche dabei, Datenschutz und Vertraulichkeit nicht nur rein technisch zu betrachten, sondern in Organisationen bereits frühzeitig beim Entwurf von Geschäftsprozessen zu berücksichtigen. Die in dem Buch veröffentlichte Petri-Netz-Erweiterung ICN ermöglicht es, Vertraulichkeit und Aspekte des Datenschutzes innerhalb von Geschäftsprozessmodellen systematisch zu betrachten

Analysing Privacy Aspects of Business Processes using Augmented Process Mining Results

Data protection requirements must be complied with when designing and executing business processes. Since organizations usually execute several processes with a large number of instances, systematic verification of compliance is difficult. Process mining offers a solution to this problem. However, existing process mining methods do not take into account information about the types of personal data processed and their reasons (permissions). For this problem, the augmentation of process mining results offers a promising approach. Process models generated by process mining are subsequently augmented to Information Confidentiality and Privacy Nets. These can then be used as a basis for compliance checking

Literature Survey on how to cluster and define Living Labs, Real World Laboratories and similar research infrastructures

In today\u27s world, where societal challenges in the areas of digitalization, demographic change and sustainability are becoming increasingly complex, new innovation structures are needed to meet these challenges. Living Labs or also Real World Laboratories prove to be such. Through their applied methods such as co-creation, they integrate users into research, making it more user-centric. Which other research infrastructures exist and how they can be differentiated is presented in this paper on the basis of a systematic literature research. Furthermore, methods for user integration are examined and provided in the form of an overview

Software design patterns for ai-systems

Well-established design patterns offer the possibility of standardized construction of software systems and can be used in various ways. The systematic use of design patterns in the field of Artificial Intelligence (AI) Systems however, has received little attention so far, despite AI being a popular research area in recent years. AI systems can be used for a wide variety of applications and play an increasingly important role in business and everyday life. AI systems are becoming more complex however, the actual machine learning (ML) task comprises only a small part of the total source code of a system. In order to maintain a clear and structured architecture for such systems and to allow easy maintenance, standardized elements should be reused in the design. This paper describes possible applications of well-known design patterns in AI systems to improve traceability of the system design

Herausforderungen bei der Entwicklung von Anwendungen zum Selbstdatenschutz

Durch zunehmende Vernetzung steigt die Notwendigkeit, Betroffene in der Ausübung ihres Privatsphärenschutzes zu unterstützen. Wenn technische Lösungen dabei in die Integrität urheberrechtlich geschützter Computerprogramme eingreifen oder Geschäftsmodelle „Dienst gegen Daten“ vereiteln, stellt sich die Frage der urheber-und lauterkeitsrechtlichen Bewertung im Spannungsverhältnis zum Selbstdatenschutz. In diesem Beitrag werden die rechtlichen Herausforderungen, auch im Hinblick auf aktuelle Vorschläge der Datenökonomisierung, beleuchtet und mit einem Exkurs zu den Anforderungen der intelligenten Bereitstellung generierter Daten zur Verschleierung persönlicher Informationen abgeschlossen

Citizen Empowerment by a Technical Approach for Privacy Enforcement

It is a fundamental right of every natural person to control which personal information is collected, stored and processed by whom, for what purposes and how long. In fact, many (cloud based) services can only be used if the user allows them broad data collection and analysis. Often, users can only decide to either give their data or not to participate in communities. The refusal to provide personal data results in significant drawbacks for social interaction. That is why we believe that there is a need for tools to control one\u27s own data in an easy and effective way as protection against economic interest of global companies and their cloud computing systems (as data collector from apps, mobiles and services). Especially, as nowadays everybody is permanently online using different services and devices, users are often lacking the means to effectively control the access to their private data. Therefore, we present an approach to manage and distribute privacy settings: PRIVACY-AVARE is intended to enable users to centrally determine their data protection preferences and to apply them on different devices. Thus, users gain control over their data when using cloud based services. In this paper, we present the main idea of PRIVACY-AVARE