Christian Richthammer

Trust and Big Data: A Roadmap for Research

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 trust-related 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 inter-dependencies. 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.

Visualizing Unfair Ratings in Online Reputation Systems

Reputation systems provide a valuable method to measure the trustworthiness of sellers or the quality of products in an e-commerce environment. Due to their economic importance, reputation systems are subject to many attacks. A common problem are unfair ratings which are used to unfairly increase or decrease the reputation of an entity. Although being of high practical relevance, unfair rating attacks have only rarely been considered in literature. The few approaches that have been proposed are furthermore quite non-transparent to the user. In this work, we employ visual analytics to identify colluding digital identities. The ultimate benefit of our approach is the transparent revelation of the true reputation of an entity by interactively using both endogenous and exogenous discounting methods. We thereto introduce a generic conceptual design of a visual analytics component that is independent of the underlying reputation system. We then describe how this concept was implemented in a software prototype. Subsequently, we demonstrate its proper functioning by means of an empirical study based on two real-world datasets from eBay and Epinions. Overall, we show that our approach notably enhances transparency, bares an enormous potential and might thus lead to substantially more robust reputation systems and enhanced user experience.

Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control

The growing relevance and usage intensity of Online Social Networks (OSNs) along with the accumulation of a large amount of user data has led to privacy concerns among researchers and end users. Despite a 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, hence leading to confusion in related discussions. This paper proposes a taxonomy for data types on OSNs based on a thorough literature analysis and a conceptualization of typical OSN user activities. It aims at clarifying discussions among researchers, benefiting comparisons of data types within and across OSNs and at educating the end user about characteristics and implications of OSN data types. The taxonomy is evaluated by applying it to four major OSNs.

Reusable components for online reputation systems

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.

Explorative Analysis of Recommendations Through Interactive Visualization

Even though today’s recommender algorithms are highly sophisticated, they can hardly take into account the users’ situational needs. An obvious way to address this is to initially inquire the users’ momentary preferences, but the users’ inability to accurately state them upfront may lead to the loss of several good alternatives. Hence, this paper suggests to generate the recommendations without such additional input data from the users and let them interactively explore the recommended items on their own. To support this explorative analysis, a novel visualization tool based on treemaps is developed. The analysis of the prototype demonstrates that the interactive treemap visualization facilitates the users’ comprehension of the big picture of available alternatives and the reasoning behind the recommendations. This helps the users get clear about their situational needs, inspect the most relevant recommendations in detail, and finally arrive at informed decisions.

Reusable Defense Components for Online Reputation Systems

Attacks on trust and reputation systems (TRS) as well as defense strategies against certain attacks are the subject of many research papers. Although proposing valuable ideas, they all exhibit at least one of the following major shortcomings. Firstly, many researchers design defense mechanisms from scratch and without reusing approved ideas. Secondly, most proposals are limited to naming and theoretically describing the defense mechanisms. Another issue is the inconsistent denomination of attacks with similar characteristics among different researchers. To address these shortcomings, we propose a novel taxonomy of attacks on TRS focusing on their general characteristics and symptomatology. We use this taxonomy to assign reusable, clearly described and practically implemented components to different classes of attacks. With this work, we aim to provide a basis for TRS designers to experiment with numerous defense mechanisms and to build more robust systems in the end.

Reputation-Enhanced Recommender Systems

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.

Interactive Visualization of Recommender Systems Data

Recommender systems provide a valuable mechanism to address the information overload problem by reducing a data set to the items that may be interesting for a particular user. While the quality of recommendations has notably improved in the recent years, the complex algorithms in use lead to high non-transparency for the end user. We propose the usage of interactive visualizations for presenting recommendations. By involving the user in the information reduction process, the quality of recommendations could be enhanced whilst keeping the systems transparency. This work gives first insights by analyzing recommender systems data and matching them to suitable visualization and interaction techniques. The findings are illustrated by means of an example scenario based on a typical real-world setting.

Personalized Composition of Trustful Reputation Systems

The vast amount of computation techniques for reputation systems proposed in the past has resulted in a need for a global online trust repository with reusable components. In order to increase the practical usability of such a repository, we propose a software framework that supports the user in selecting appropriate components and automatically combines them to a fully functional computation engine. On the one hand, this lets developers experiment with different concepts and move away from one single static computation engine. On the other hand, our software framework also enables an explorative trust evaluation through user interaction. In this way, we notably increase the transparency of reputation systems. To demonstrate the practical applicability of our proposal, we present realistic use cases and describe how it would be employed in these scenarios.

Privacy in Social Networks: Existing Challenges and Proposals for Solutions

The significant change in our social lives and communication habits caused by the rise of Social Network Sites (SNSs) has not only brought along benefits but is also accompanied by privacy threats. In this paper we present our research efforts on SNS privacy and social identity management. First, we outline the results of an empirical study showing significant discrepancies between Facebook users’ actual privacy settings and their perception as well as their preferences. Based on this evident need for improving privacy, we present a novel conceptualization of privacy that serves as the basis for tackling the challenges. Finally, the paper provides an overview of solutions we developed as part of our research efforts on privacy in SNSs.