Interactive Reputation Systems
Early reputation systems use simple computation metrics that can easily be manipulated by malicious actors. Advanced computation models that mitigate their weaknesses, however, are non-transparent to the end-users thus lowering their understandability and the users’ trust towards the reputation system. The paper proposes the concept of interactive reputation systems that combine the cognitive capabilities of the user with the advantages of robust metrics while preserving the system’s transparency. Results of the evaluation show that interactive reputation systems increase both the users’ detection ability (robustness) and understanding of malicious behavior while avoiding trade-offs in usability.
KeywordsTrust reputation systems Information security Visual analytics
Reputation systems are an essential component of various online platforms such as electronic marketplaces, e-commerce websites, or file-sharing systems. Since these environments usually involve a huge number of strangers who have not interacted before, users do not have any information about e.g. the trustworthiness of a seller or the quality of a product – ex ante. To solve this problem, reputation systems encourage users to provide feedback on past experiences. They then compute one or several reputation values and display the output in an actor’s or item’s feedback profile. In this way, users can establish trust based on the experiences made by others (Resnick et al. 2000; Awad and Ragowsky 2008).
In the recent years, this simple concept has even generated websites that particularly focus on collecting reviews for different use cases such as tripadvisor1 or yelp2. The success of these websites is hardly surprising, as researchers have been able to provide empirical evidence that reputation systems manage to reduce information asymmetry and increase a market’s efficiency (Dellarocas 2001; Yamagishi and Matsuda 2002). On eBay, sellers with better reputation even have an increased number of sales and obtain higher prices (Diekmann et al. 2013). While the concomitant economic value of “good” reputation encourages trustworthy participation, it also offers an enticement for malicious actors to exploit the weaknesses of reputation systems in order to appear more reputable than appropriate. For that reason, the robustness3 of reputation systems has become a focal issue in the trust management research community (Jøsang 2012).
Since most early reputation systems could easily be manipulated, researchers have started to introduce a large number of computation models that are robust against a variety of different attacks. In most cases, these models are based on advanced mathematics and have thus become quite complex (Marsh et al. 2012). High complexity and resulting non-transparency, however, lower the users’ trust towards the reputation systems (Hammer et al. 2013). In practice, in contrast, this trend to more robust computation models have not taken effect. Common electronic marketplaces such as eBay4 or e-commerce websites like Amazon5 still use a very simple and easy to understand metric. Such reputation systems, in turn, have been shown to be very weak against several attacks.
To close the gap between robust metrics introduced in literature and transparent and easy to understand user interfaces mostly used in practice, we propose the concept of interactive reputation systems. Involving the user in reputation assessment, interactive reputation systems aim at providing enhanced insights through combining the cognitive capabilities and the experience of the users with the advantages of robust reputation metrics. While there has been considerable work on certain building blocks of interactive reputation systems [(e.g. modularizing and configuring the computation (Hillebrand and Coetzee 2015; Sänger et al. 2015c) or displaying reputation data in interactive visualizations (Sänger et al. 2015b)], the work presented in this paper is the first to the best of our knowledge that connects the distinct building blocks to introduce the complete and integrated concept of interactive reputation systems.
The remainder of this paper is based on the design science research paradigm (Hevner et al. 2004) following the phases introduced by Peffers et al. (2007) (problem identification and motivation, objective of the solution, design and development, demonstration, evaluation, communication): first, we describe the background of online trust and reputation systems as well as robust metrics and transparent presentation in Sect. 2 (problem identification and motivation). Based on that, we point out the research gap and define the objectives of our work (objective of the solution). In Sect. 3 we introduce the concept of interactive reputation systems. Here, we propose the process model and describe how the single building blocks can be integrated (design and development). To demonstrate how interactive reputation systems could look in practice we present a software prototype (demonstration). In order to evaluate the quality of our solution, we conducted several experiments as well as an international user study that are described in Sect. 4 (evaluation). Results show that through involving the user in reputation assessment, attacks on reputation systems can be reliably detected while maintaining the system’s transparency. In this way, the users’ understanding of malicious behavior can be enhanced. Finally we sum up our contribution and discuss the implications of our findings in the Sect. 5 (communication).
2 Background and Objectives
2.1 Online Trust and Reputation Systems
In search of a value describing Gambetta’s subjective probability or what is called trustworthiness, reputation is often used as a measure (Jøsang et al. 2007). Reputation-based trust is deduced from past experiences or behavior of an entity. Since a common online environment may involve millions of actors, it can be difficult to determine a person’s reputation manually. For this reason, reputation systems have become popular tools to support reputation assessment. Reputation systems encourage users to leave feedback, e.g., about the performance of a seller or the quality of a product. They then collect all feedback created, aggregate the ratings to compute one or several reputation values and present the output in a feedback profile (Resnick et al. 2000). Figure 1 depicts the generic process of a reputation system. Here, we also added the user who employs the reputation system to come to a decision (e.g. whether to buy or not to buy from a specific seller).
2.2 Exploiting Reputation Systems
Example attacks on reputation systems
The attacker provides many high ratings to unfairly push the reputation of an entity
The attacker provides many low ratings to unfairly destroy the reputation of an entity
Value imbalance exploitation
The attackers gathers good reputation selling cheap items, but at the same time cheats on the expensive ones
The attackers first acts honestly to build high reputation, than “milks” the good reputation. After a certain threshold value is reached, he behaves honestly again and starts from the beginning
The attacker behaves maliciously from the beginning. After he has received negative ratings, he opens a new account
The attacker creates many accounts (Sybils) at the same time to increase his influence in a community
In the recent years, a huge variety of different attacks on reputation systems has been described in literature (Jøsang and Golbeck 2009; Sun and Liu 2012). In order to give a structured overview of weaknesses, Koutrouli and Tsalgatidou (2012) identified three classes of attacks, namely unfair feedback, inconsistent behavior and identity-based attacks. In unfair feedback attacks, malicious peers provide many unfairly high or low ratings to e.g. push the reputation of a friend or destroy the reputation of a competitor (Dellarocas 2000). The second class – inconsistent behavior – involves attacks that benefit from the characteristics of specific computation metrics. In reputation systems that do not consider transaction context, for instance, a malicious actor could show a discriminating behavior in different situations such as selling high quality chewing gum but low quality laptops. Thirdly, identity-based attacks primarily address systems that allow cheap pseudonyms. Table 1 gives an overview of selected example attacks.
To cope with the weaknesses of early reputation systems, a large body of work on the design of new trust models and robust metrics has been published (Artz and Gil 2007; Koutrouli and Tsalgatidou 2012; Sun and Liu 2012). While most early computation models could easily be manipulated by malicious actors, current models (particularly those introduced in literature) are quite resistant against a variety of attacks.
2.3 Robust Metrics vs. Transparent Presentation
Example reputation models
Beta reputation system, TRAVOS
Beta probability density functions (PDF)
Liu et al. (2011)
Yu and Singh (2002)
Belief model, Dempster-Shafer theory
Web services reputation
Malik et al. (2009)
Hidden Markov Model
Sabater and Sierra (2001)
Admittedly, these sophisticated models are more robust when attacked, however, they are beyond the understanding of the users intended to apply them in most cases (Marsh et al. 2012). One or several numerical values as only outcomes of the computation process are in general not sufficiently transparent to the end-user as they cannot convey any details about the exact input data (e.g. which reviews were considered, which not?) leading to a cognitive gap. A recent user study conducted by Hammer et al. (2013) in which they compared two reputation metrics was able to show that more than half of the participants criticized this lack of transparency if they were not able to understand the computation.
2.4 Research Gap and Objectives
Based on the problems identified above, the main research question (RQ) addressed in this work is stated as follows:
RQ. How can the robustness of reputation systems be enhanced without reducing both transparency and the user’s understanding of reputation data?
To increase the user’s ability to detect attacks on reputation systems (robustness), and
to enhance the user’s understanding of reputation data.
2.5 Research Approach
When designing a complex artifact, there may not be a definite final condition at the beginning. In line with the human problem solving approach (Newell 1972), the analysis of a design problem therefore involves the successive decomposition to sub-problems until they seem solvable, followed by a final composition of the single sub-solutions to an overall solution (Chandrasekaran 1990). With this in mind, we first decomposed the overall problem to two sub-problems, each addressing one of the two central phases of the generic process of a reputation system: computation and presentation. On the one hand, we elaborated on the question of how the computation could be re-designed as an interactive process (Sänger et al. 2015a, c). On the other hand, we analyzed how reputation data could be presented to involve the user in reputation assessment (Sänger and Pernul 2014, 2016; Sänger et al. 2015b). In these previous works, however, the two phases – computation and presentation – were considered as isolated building blocks.
In this paper, we go one step beyond the state of the art by connecting the results of our recent works (sub-solutions) and introduce the overall concept of interactive reputation systems. Only after combining the findings made for the respective blocks, the idea of involving the user in reputation assessment through interaction can take full effect. First, we will introduce and define the notion of an interactive reputation system and describe the process model. Second, based on the findings made for interactive computation and interactive presentation, we will integrate both blocks on a conceptual level. After that, we show what an interactive reputation system could look in a real-world environment by means of a software prototype and evaluate this concept as to the objectives defined above.
3 Interactive Reputation Systems
3.1 Process Model
A classical reputation system delivers static numerical reputation values as output. Though the user interface may provide some filters to read specific reviews (e.g. only negative feedback), the reputation value is not adjusted.
A few novel reputation systems that have emerged in the recent years, however, provide the possibility to adapt single settings and reflect these changes accordingly in the reputation value. Tripadvisor, for example, allows the users to focus on reviews provided by a specific peer group (e.g. families, business travelers, etc.), filters the corresponding reviews and displays an aggregated reputation value that only involves ratings of the selected group. We call such systems interactive reputation systems, since unlike static systems these systems encourage the user to interact and adapt the computation levers in order to gain enhanced insights. Based on this, the definition of an interactive reputation system as used in this work is as follows:
An interactive reputation system is a reputation system that allows the users to dynamically adapt the underlying computation mechanism.
Exactly as in the process model of a classical reputation system (see Fig. 1), data is collected in the first step. Second, both the raw data as well as models of the data calculated in the computation are presented to the end-user. Ideally, a very intuitive metric such as the average rating value is used in the initial computation to provide a transparent starting point for further analyses. On eBay, for instance, a list of the textual reviews (raw data), the share of positive ratings and an aggregation of positive, neutral and negative ratings for specific time-frames (models) are presented in the feedback profile interface. In an interactive reputation system, the user can then “interact” with the visual representation of the reputation profile e.g. through adapting computation settings, thus changing the models that are computed. Typical interaction techniques include the filtering of unnecessary reviews, adding of weighting rules or changing of the entire aggregation metrics. The inherent user-driven definition of the calculation rules will logically lead to a clearer understanding of the models computed.6
3.2 Conceptual Design
Computation: To transform the computation mechanism from static to dynamic in a way that it may be interactively adapted by the user, we chose a modular design that allows to dynamically add and remove components.
Presentation: The user-friendly and transparent presentation of reputation data is a vital requirement to guarantee the proper understanding of the information provided to the end-user. Adding interactive visualizations of reputation data to the user interface provides a promising alternative to overcome the shortcomings of a single numerical value and link computation and presentation.
On the primary level it involves the three primary classes “filtering”, “weighting” and “aggregation”. Obviously, the first task when assessing reputation is to filter all information available for what is relevant for the specific situation. Secondly, the significance of single referrals needs to be determined such that e.g. very new feedback might be treated as more relevant than very old feedback (weighting). Finally the input data is aggregated to provide a final output (e.g. reputation value). Based on these three main classes, 14 secondary component classes, 26 components and 36 subsets were identified (Sänger et al. 2015c). Here, the components and sub-sets represent examples of how the functional blocks were instantiated in different trust models. The secondary class “simple arithmetic”, for instance, contains components that implement different simple aggregation techniques such as the “average rating value” or the “share of positive ratings”.
Secondly, we implemented a component repository that stores an example implementation and a structured description (ID, type, URL, parameter, example call, example output) of each component as reusable web services.7 The component repository (centrally stored) is publicly available and encourages researchers to add novel components and extend existing ideas. All web services implement well-defined interfaces and can easily be called using the “WebserviceCallHelper” – a helper class available on the repository website.
By dynamically combining these components through interaction (adding, removing or adapting), a user can compose his individual computation mechanism on-the-fly. To recreate Tripadvisor’s computation mechanism, for instance, only the two components “context-based filter” and “average rating value” of the classes “filtering (primary class), attribute-based filter (secondary class)” and “aggregation (primary class), simple arithmetic (secondary class)” need to be selected and composed. Overall, over 170,000 distinct combinations of the computation components are possible.
A numerical value as only outcome of the computation lacks transparency. Therefore, we chose a different path by additionally developing an integrated visual representation of reputation data using interactive visualizations (Sänger and Pernul 2016). Visualizations are very convenient to depict a wide range of information in one integrated view.
3.2.3 Integrating Computation and Presentation
Finally, the buyer wants to focus on high price stamps. Through clicking on the “price” label, a weighting factor ranging from [0;1] is added to each rating (0 for the lower end and 1 for the upper end of the scale). Accordingly, the opacity of the polylines is adapted. On computation level, this is equivalent to adding a context-based weighting component. The corresponding reputation value is again dynamically updated to 30.2% positive ratings (see Fig. 9).
This example reveals that displaying reputation data in an interactive visualization and encouraging the user to add filtering and weighting rules through user interaction may notably increase the transparency and understanding of a rather complex computation model. If a reputation value of 30.2% based on a context-aware metric was provided initially, in contrast, a huge variety of potentially useful and interesting information would have been omitted. It would neither be clear which reviews were filtered nor which weighting factors were allocated to the different ratings.
3.3.1 A Generic Software Framework
Taking a closer look at the detailed architecture of the logic layer, we find two main building blocks. On server side the “dynamic composition webservice” (Laravel) represents the computation phase. On client side, the “dynamic evaluation client” represents the presentation phase. When the website is initially built, all raw reputation data is handed over to the dynamic evaluation client. Here, the analytics handler implemented in AngularJS takes all input data and builds the front-end website involving interactive visualizations (d3). In order to provide an initial reputation value, the “dynamic composition webservice” is called up. It ensures the composition of the computation engine, the calculation of reputation values and the delivery of output data to the client. After receiving a pre-defined or user-defined configuration as well as a list of rating data as input, the workflow engine validates the configuration and processes the defined combination. Here, all necessary computation components (web services) are sequentially called up. In our implementation, the dynamic composition web service as well as all computation components (web services) are stored in the central service repository introduced in Sect. 3.2.1. Once a reputation value is calculated, the output is sent to the client. A data exchange between client and server is carried out using JSON-format. After the initial reputation value is calculated, the website has loaded completely and is presented to the user.
Whenever the user starts to interact with a visualization, the analytics handler directly updates the composition configuration, sends it to the dynamic composition webservice and receives an updated output (reputation value) as an answer. In this way, interactions with the visualization can be directly represented in an updated reputation value.
To give an impression of what interactive reputation systems could look in practice, this paper presents a prototype (using real-world data) that was implemented based on the software framework described above. The prototype shows an example of how an existing reputation system could be adapted to enable user interaction.
3.3.2 Prototype: Extension of an eBay-like Feedback Profile
Similar to eBay, a summary of seller behavior is given in the top. In the middle, the parallel coordinates visualization (left) as well as the relative share of positive, neutral and negative ratings in a bar-chart (right) provide an overview of feedback and transaction context. Below the detailed feedback is listed. For interactivity, we added a “context-based filtering” component that is called up when parts of the axes are highlighted through “brushing”. The reputation values (right) are additionally visualized in a bar-chart. Figure 11 shows an example screenshot of the prototype. Selecting ranges on the axis of the parallel coordinates visualization, the polylines displaying tuples that are not within the specified boundaries as well as the corresponding reviews are hidden. At the same time, all reputation values are dynamically updated. In this way, users can dive into the data and easily reveal correlations (Fig. 12 of the case study shows an example where high price transaction are highlighted).
To measure the quality of our concept, several evaluations have been conducted that involve both a real world case and an international user study. While the case proves that interactive reputation systems notably support the detection of malicious behavior if correctly applied, the user study provides empirical evidence that this concept is also beneficial for the average end-user.
4.1 Case Study
Actors who profit from the value imbalance problem build a high reputation by selling cheap products while cheating on the expensive ones. For this first case study, real world data from eBay Germany was gathered. The seller feedback profile introduced in this case listed 136 positive, 0 neutral and 8 negative reviews resulting in an average of 94.4% positive ratings in the last 12 months. The profile was found through a thread in eBay’s community boards, where buyer complained that he had not yet received any goods although having paid a month ago.
The very fact that the malicious seller managed to cheat on 8 buyers and that his account was still active points out that eBay’s current seller profiles are not capable of revealing such attacks. The representation of ratings and corresponding transaction context in an interactive reputation system, however, is a very intuitive way to support the detection of malicious behavior and prevent deception (objective 1). By instead using a context-sensitive reputation metric instead that provides a low reputation value for high price transaction, malicious behavior might also have been prevented. A single numerical value, though, could not have revealed the overall coherence and hence grant transparency.
4.2 International User Study
The case study described above demonstrates that interactive reputation systems can theoretically support the detection of malicious behavior. However, so far it has not been experimentally verified that our approach indeed increases the understanding of reputation data and can be applied by the average end user. Therefore, a controlled between-subject experiment12 with 40 UK and 41 German participants (with different backgrounds and experience) in which we analyzed the users’ detection ability and understanding of malicious behavior in an electronic marketplace (Sänger et al. 2016) was conducted. The study was carried out at University College London (UCL) and Friedrich Alexander University Erlangen-Nuremberg (FAU). In order to avoid priming the participants for fraud detection, they were asked to take part in a “usability test of online marketplace features”. The study was divided into three phases. In the pre-study phase, the participants answered some initial questions regarding their experience with electronic marketplaces. During the main part of the study, participants had to solve four cases. In each case they were asked to buy a specific item (e.g. a mobile phone for 500€), to have a look at the feedback profiles of two sellers who offered the item and to give a preference for one seller. One of two sellers was honest while the other was malicious (participants were not told before to not prime them for fraud detection). The malicious seller showed a discriminating behavior for different context attributes in each case such as a high rate of negative ratings for high price items, whereas the honest seller behaved consistently within the entire context. The treatment group used our prototype (new interface condition) while the control group used an eBay-like interface that did not involve an interactive visualization and a dynamic reputation value (old interface condition). To obtain an overview of the UI, both groups had to watch a short video introducing the basic functions of the respective interface. In the third phase, participants were asked about the perceived usability by means of the System Usability Scale (Brooke 1996). Furthermore participants using the new interface provided details on the perceived usefulness. At the end, all participants were debriefed.
Participants were recruited via a standardized participant recruitment website, an internal mailing list as well as internal flyering. The age of the participants ranged from 18 to 41 years and 35 were female. For each case, participants reported a preference for one of both sellers on a 5-point likert scale with 3 representing no preference. We recoded the scale to +2 (preference for the honest seller) to −2 (preference for the malicious seller). Results show that the detection ability using the new interface was significantly higher13 (77%) opposed to the old interface (56%), although the same amount of information was presented in both interfaces (objective 1). A positive value on the recoded preference scale was considered a correct detection. Repeating this measure with online buying experience as a further independent variable, we found that this effect was even more pronounced. When using the old eBay-like interface, inexperienced participants were not able to detect any malicious behavior in 3 out of 4 cases. Participants reported their lifetime purchases on electronic marketplaces and we used the median cutoff score to categorize them as experienced or inexperienced.
Results of the user study
Correct detection of malicious seller
Preferences for honest (of user who decided to buy)
Preferences for malicious (of user who decided to buy)
No preference (of user who decided to buy)
Sensemaking Score [0; 1]
Furthermore, participants provided oral and written accounts for their decisions. Here, independent raters checked whether these reasons contained a priori specified criteria for determining that one seller is malicious and the other is honest. A “Sensemaking Score” was calculated taking the number of mentioned criteria divided by the number of all possible criteria. We found that users who used the new interface had a significantly higher Sensemaking Score (0.46 opposed to 0.25) indicating that they had a better understanding of seller behavior (objective 2). Finally, we asked participants about the perceived usability and the perceived usefulness of the new interface. Overall, participants reported the new system to be superior regarding a range of characteristics without trade-offs in usability, suggesting that such a system might be quickly adopted by users due to its high perceived usefulness.
5 Discussion and Conclusion
In this work, we introduced the notion of an interactive reputation system that tries to involve the user in reputation assessment to increase the users’ detection ability and understanding of malicious behavior. We first delineated the generic process model, described how we designed and implemented the single building blocks, gave details on the integration of both blocks and presented an example prototype. The evaluation showed that both objectives – the improvement of the detection ability (robustness) and the enhancement of the users’ understanding could be met without trade-offs in usability.
According to Zhang et al. (2012b) – who measured the robustness of a number of trust models in a recent work – “the more robust the trust model, the larger the transaction volume difference between the honest and dishonest duopoly seller”. Reviewing the results of the user study, there is a difference of 0.78 (85–7% for the new interface) compared to 0.28 (58–30% old interface) between participants who decided to buy from one of the sellers. We assumed that users with no preference randomly choose one of the sellers and can thus be left out of consideration. This leads to an increase of 178% in terms of robustness. Measures of the Sensemaking Score also indicate an increase of 84% (0.46 opposed to 0.25) in the sense of understanding of reputation data.
While the concept of interactive reputation systems is in the early stages of development, this work provides some promising insights and a new perspective on how the robustness of reputation systems could be addressed while maintaining their transparency. The use of interactive visualizations for displaying reputation data and involving the user in reputation assessment proved to be valuable not only for expert use but also for the average end-user. Example implementations such as the prototype presented in this work show that current reputation systems do not necessarily need to be completely changed, but could also be extended to interactive functionality. Users reported such an extension to be very useful as demonstrated in the user study.
This work also has some limitations: In the evaluation, the experiments only focused on the usage of one specific prototype implementing one interactive visualization. Also, we analyzed the detection of specific attacks. Future research should therefore involve a larger variety of different prototypes implementing distinct visualization and interaction techniques as well as other facets of malicious behavior (e.g. unfair ratings). As the pool of participants who took part in the user study may not have been a perfectly representative sample (the majority had an academic background), further studies using different prototypes as well as a broader pool of participants may lead to insights that are even more substantial. Here, it is particularly interesting if end-users can deal with more complex interactive visualizations as well as more complex attacks. Moreover, participants being asked to “solve” cases may have analyzed the seller profiles more critically than in a real-life situation. Here, field studies outside of a lab setting may be a necessary step. Overall, more extensive prototyping as well as more comprehensive experiments will help to complete the design science cycle in an even more rigorous way.
One further issue that should be covered by future work are novel attacks and weaknesses that result from displaying reputation data in visualizations or involving the user in reputation assessment. As reputation systems are constantly under attack and malicious behavior is constantly adapted to new conditions (new threats may arise), a detailed analysis of possible new weaknesses may help to prevent simple exploits.
Overall, due to the practical relevance of this problem area and the promising outcomes we hope that our work encourages the conduct of further research on interactive reputation systems.
A robust system always provides a higher reputation value for an honest actor compared to a malicious actor who tries to exploit the system.
This process model is strongly inspired by the visual analytics process model that tries to combine the visual-cognitive capabilities of a human analyst and the computing power of a machine (Keim et al. 2010).
The study was conducted in collaboration between Johannes Sänger (University of Regensburg), Norman Hänsch (FAU), Brian Glass (UCL), Zinaida Benenson (FAU), Robert Landwirth (FAU) and M. Angela Sasse (UCL).
\(\chi ^2 (2, N=324) = 16.44,\, p<0.001.\)
There was no significant main effect on the decision to buy between both conditions for each case (ANOVA).
\(\chi ^2\) on the decision to buy revealed that those in the new interface condition were more likely to select the honest seller \(\chi ^2 (2,N=188) = 18,85,\, p<0.001.\)
The research leading to these results was supported by the “Bavarian State Ministry of Education, Science and the Arts” as part of the FORSEC research association.
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