Abstract-We are currently living in the age of Big Data coming along with the challenge to grasp the golden opportunities at hand. This mixed blessing also dominates the relation between Big Data and trust. On the one side, large amounts of trustrelated data can be utilized to establish innovative data-driven approaches for reputation-based trust management. On the other side, this is intrinsically tied to the trust we can put in the origins and quality of the underlying data. In this paper, we address both sides of trust and Big Data by structuring the problem domain and presenting current research directions and interdependencies. Based on this, we define focal issues which serve as future research directions for the track to our vision of Next Generation Online Trust within the FORSEC project.
Abstract. The rise of online social networks (OSNs) has traditionally been accompanied by privacy concerns. These typically stem from facts: First, OSN service providers' access to large databases with millions of user profiles and their exploitation. Second, the user's inability to create and manage different identity facets and enforce access to the self as in the real world. In this paper, we argue in favor of a new paradigm, decoupling the management of social identities in OSNs from other social network services and providing access controls that take social contexts into consideration. For this purpose, we first propose Priamos, an architecture for privacy-preserving autonomous management of social identities and subsequently present one of its core components to realize contextaware access control. We have implemented a prototype to evaluate the feasibility of the proposed approach.
We are currently living in the age of big data with ever growing volumes of heterogeneous and fast moving data. Whether they are mobile devices, internal or external systems or cloud-based systems data is generated, stored, processed and distributed in many different systems. This leads to various information security and privacy risks. To address these issues, especially from the viewpoint of data management and data governance we propose a conceptual analysis model. Thereby, our model takes into account the dimension of data storage location together with their respective risks and costs while considering the strategic value and sensitivity of data assets. For demonstrating our approach we developed a visual analytics web application which is based on parallel sets visualizations. By being able to interactively explore the analysis dimensions users are supported in developing enhanced situational awareness for making decisions in the context of secure and economical data storage.
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