Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.

Reputation systems have been extensively explored in various disciplines and application areas. A problem in this context is that the computation engines applied by most reputation systems available are designed from scratch and rarely consider well established concepts and achievements made by others. Thus, approved models and promising approaches may get lost in the shuffle. In this work, we aim to foster reuse in respect of trust and reputation systems by providing a hierarchical component taxonomy of computation engines which serves as a natural framework for the design of new reputation systems. In order to assist the design process we, furthermore, provide a component repository that contains design knowledge on both a conceptual and an implementation level. To evaluate our approach we conduct a descriptive scenario-based analysis which shows that it has an obvious utility from a practical point of view. Matching the identified components and the properties of trust introduced in literature, we finally show which properties of trust are widely covered by common models and which aspects have only rarely been considered so far.

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.

Recommender systems are pivotal components of modern Internet platforms and constitute a well-established research field. By now, research has resulted in highly sophisticated recommender algorithms whose further optimization often yields only marginal improvements. This paper goes beyond the commonly dominating focus on optimizing algorithms and instead follows the idea of enhancing recommender systems with reputation data. Since the concept of reputation-enhanced recommender systems has attracted considerable attention in recent years, the main aim of the paper is to provide a comprehensive survey of the approaches proposed so far. To this end, existing work are identified by means of a systematic literature review and classified according to carefully considered dimensions. In addition, the resulting structured analysis of the state of the art serves as a basis for the deduction of future research directions.

One major shortcoming of traditional recommender systems is their inability to adjust to users' short-term preferences resulting from varying situation-specific factors. To address this, we propose the notion of situationaware recommender systems, which are supposed to autonomously determine the users' current situation based on a multitude of contextual side information and generate truly personalized recommendations. In particular, we develop a situation awareness model for recommender systems, include it in a situationaware recommendation process, and derive generic design steps for the design of situation-aware recommender systems. The feasibility of these concepts is demonstrated by directly employing them for the development and implementation of a music recommender system for everyday situations. Moreover, their meaningfulness is shown by means of an empirical user study. The outcomes of the evaluation indicate a significant increase in user satisfaction compared to traditional (i.e. non-situation-aware) recommendations.