Christian Hütter

A flexible architecture for privacy-aware trust management

In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.

Peer production of structured knowledge -: an empirical study of ratings and incentive mechanisms

Creating and maintaining semantic structures such as ontologies on a large scale is a labor-intensive task, which a sole individual cannot perform. Established automated solutions for this task do not yet exist. Peer production is a promising approach to create structured knowledge: Members of an online community create and maintain semantic structures collaboratively. To motivate members to participate and to ensure the quality of the data, rating-based incentive mechanisms are promising. Members mutually rate the quality of their contributions and are rewarded for good contributions and truthful ratings. Until now, there has been no systematic evaluation of such rating mechanisms in the context of structured knowledge. We have developed a platform for the collaborative creation of semantic structures. To evaluate the effect of ratings and incentive mechanisms on the quality of peer-produced data, we have conducted an extensive empirical study in an online community. We show that ratings are a reliable measure of the quality of contributions by comparing user ratings with an ex post evaluation by experts. Further experimental results are that incentive mechanisms increase the quality of contributions. We conclude that ratings and incentive mechanisms are promising to foster and improve the peer production of structured knowledge.

Peer-Supervised Learning with Built-In Quality Control Based on Multiple-Choice Questions: A Case Study

Rating multiple choice questions (MCQ) created by peers has been touted as a good approach to peer assessment. The main challenge in this setting is to ensure the quality of peer assessment. Existing approaches rely on the assumption that students intrinsically create high-quality ratings. We propose an incentive mechanism to increase the quality of ratings. To evaluate our approach, we have conducted a case study with 242 students and 17 experts. Our results show that peer ratings are a good predictor of expert ratings. Furthermore, we develop a model that reliably measures the performance of students, but does not require expert ratings.

SONAR: Towards User-centric Social Network Analysis and Visualization

Social network analysis (SNA) has attracted a lot of attention over the past years. Existing tools for SNA do not allow a user-centric analysis of the social neighborhood, i.e., the subgraph of the users friends and friends of a friend. In this paper, we introduce SONAR, an open source Web application for user-centric SNA. Its extensible architecture and flexible data model allows developers to embed SONAR directly into social networking websites. A performance evaluation shows that our application scales well with the number of users and adds only minimal overhead to the SNA algorithms.

Strategic Provider Selection in a Policy-Based Helping Scenario

In service-oriented computing, software agents interact by requesting and providing services. Since providing a service incurs cost, uncooperative behavior dominates in the absence of an incentive mechanism. An economic model that describes interactions between individuals is the Helping Game. There, pairs of requester and provider are randomly matched. In various real-world applications in turn, several providers offer similar services, and requesters have a choice of providers. The rationale behind strategic provider selection is to choose the provider that is most likely to perform the task as desired. The results from existing studies of the Helping Game are not directly applicable to settings with provider selection. To analyze how strategic provider selection affects the efficiency of enterprise systems, we have designed and carried out an experimental study. Our results show that cooperative participants receive significantly more requests than uncooperative ones, making cooperation expensive. We conclude that system designers must incentivize requesters to balance their tasks between providers.

Optimizing Multiple Centrality Computations for Reputation Systems

In open environments, deciding if an individual is trustworthy, based on his past behavior, is fundamentally important. To accomplish this, centrality in a so-called feedback graph is often used as a trust measure. The nodes of this graph represent the individuals, and an edge represents feedback that evaluates a past interaction. In the open environments envisioned where individuals can specify for themselves of how to derive their trust in others, we observe that several centrality computations take place at the same time. With centrality computation being an expensive operation, performance is an important issue. While techniques for the optimization of a single centrality computation exist, little attention so far has gone into the computation of several centrality measures in combination. In this paper, we investigate how to compute several centrality measures at the same time efficiently. We propose two new optimization techniques and demonstrate their usefulness experimentally both on synthetic and on real-world data sets.

Optimization Techniques for Multiple Centrality Computations

A broad range of data has a graph structure, such as the Web link structure, online social networks, or online communities whose members rate each other (reputation systems) or rate items (recommender systems). In these contexts, a common task is to identify important vertices in the graph, e.g., influential users in a social network or trustworthy users in a reputation system, by means of centrality measures. In such scenarios, several centrality computations take place at the same time, as we will explain. With centrality computation being expensive, performance is crucial. While optimization techniques for single centrality computations exist, little attention so far has gone into the computation of several centrality measures in combination. In this paper, we investigate how to efficiently compute several centrality measures at a time. We propose two new optimization techniques and demonstrate their usefulness both theoretically as well as experimentally on synthetic and on real-world data sets.

Strategic Behavior in Interaction Selection and Contact Selection

Social search platforms like Aardvark or Yahoo Answers have attracted a lot of attention lately. In principle, participants have two strategic dimensions in social search systems: (1) Interaction selection, i.e., forwarding/processing incoming requests (or not), and (2) contact selection, i.e., adding or dropping contacts. In systems with these strategic dimensions, it is unclear whether nodes cooperate, and if they form efficient network structures. To shed light on this fundamental question, we have conducted a study to investigate human behavior in interaction selection and to investigate the ability of humans to form efficient networks. In order to limit the degree of problem understanding necessary by the study participants, we have introduced the problem as an online game. 193 subjects joined the study that was online for 67 days. One result is that subjects choose contacts strategically and that they use strategies that lead to cooperative and almost efficient systems. Surprisingly, subjects tend to overestimate the value of cooperative contacts and keep cooperative but costly contacts. This observation is important: Assisting agents that help subjects to avoid this behavior might yield more efficiency.