I. INTRODUCTION. The uptake on the development and deployment of service-oriented enterprise applications on the Web has increased dramatically in recent ...
A Fuzzy Trust Management Framework for Service Web Surya Nepal, Wanita Sherchan, Jonathon Hunklinger, Athman Bouguettaya CSIRO ICT Centre, Australia {FirstName.LastName}@csiro.au
Abstract—We propose a fuzzy trust management framework for the Service Web. The proposed framework supports a natural way of representing and querying consumers’ perception on services. We describe the underlying models, algorithms and an implementation architecture, mainly focusing on the key features and contributions of the proposed framework. Keywords-Web Services; Trust Management; Reputation;
I. I NTRODUCTION The uptake on the development and deployment of service-oriented enterprise applications on the Web has increased dramatically in recent years. This has been possible due to the availability of Web services standards and technologies. The service-oriented Web (Service Web) thus becomes an attractive paradigm for future development and deployment of a wide range of services including enterprise services. A large number of enterprise services providing similar functionalities have been built and deployed on the Web. Consumers thus face a difficult task of choosing the best services that meet their requirements. This is an important issue on the Service Web as some of the services may not deliver on what they promise. This implies that a service consumer needs to trust a service to deliver the required functionality before starting the interaction. Hence, trust becomes an important and essential criteria a consumer can use in selecting the best services. A number of approaches for managing trust for Service Web has been proposed in the literature [1]. Approaches based on traditional security technologies [2], [3], [4] are not applicable to the Service Web because of its open, dynamic and distributed nature. Reputation based approaches have been proposed as a practical alternative to manage trust for Service Web [5], [6], [7], [8]. The notion underpinning the reputation-based trust models is to capture consumers’ perception of the consumed service and use it to evaluate the reputation of the service [9]. The consumers’ perception is captured using a set of Quality of Web Service (QoWS) parameters such as privacy, reliability, performance, response time and security. The set of QoWS parameters tightly dependent on the application domain. For example, a transport service may have “on time arrival and departure” as a QoWS parameter. This parameter captures a consumer’s perception on the punctuality of the provided transport service. Existing reputation-based trust management frameworks
use a feedback mechanism to capture consumers’ perception on services [10], [11]. The feedback system may allow consumers to provide their perception in a variety of ways. For example, a consumer may provide a numeric value in the scale from 1 to 10 or use a sliding bar to select an appropriate value or choose a term from a given set of linguistic terms (e.g., not reliable, reliable, very reliable, no opinion) that best describes the consumer’s perception [12]. It is possible to normalize the given feedback values and represent them within the range [0,1]. The feedback score gives a raw measure of how well a service met a consumer’s expectation in terms of quality. Therefore, the same quality of service may result in different feedback values from different consumers. For example, a consumer may give 9/10 score for a service which he/she perceived as a “highly reliable” service, whereas another consumer may give 8/10 for the same level of perception. This implies that consumers’ feedback values are subjective in nature. A consumer, who wants to know the reputation of a service, may query feedback values given by its past consumers. For example, a consumer, who wants to retrieve services that are perceived as highly reliable, can express a query as retrieve all services with reliability > 0.9. Another consumer may express the same query with the constraint reliability > 0.8. Thus, the nature of the query is also subjective. The underlying assumption in such queries is that the consumer has a knowledge of an underlying data model and its semantics. For example, the consumer knows that the maximum possible value for reliability is 1.0 in the above queries. Furthermore, the possible domain and range for each QoWS parameters could be different. The limitations of such underlying assumptions can be addressed by expressing a query using a language as close to a natural language as possible. For example, consumers who want to invoke a highly reliable service can express their query as “reliability is very important” rather than “reliability > 0.9”. Here, the term “very important” means the service has been perceived as highly reliable by its past consumers. We propose a fuzzy trust management framework for Service Web to support such queries. The proposed framework supports a natural way of representing, querying and evaluating consumers’ perception on services. The key contributions of the proposed framework can be summarized as follows. •
We propose a fuzzy trust data model for represent-
ing consumers’ perception on QoWS parameters. The defined data model thus enables the evaluation of reputation of services using consumers’ perception on QoWS parameters. • We propose a fuzzy linguistic query model along with processing algorithms for the defined data model so that consumers can express their queries without knowing the underlying data models. • Using the proposed data and query models, we have implemented a fuzzy trust management framework in the context of a travel scenario. The insights gained from the implementation suggest that the proposed framework is applicable to the Service Web. The rest of the article is organised as follows. Section 2 describes a fuzzy data model that uses the fuzzy sets theory for capturing consumers’ perception. Section 3 presents a a fuzzy linguistic query model where a numeric perception value in a query is replaced by the linguistic descriptors. Section 4 describes the query language and an evaluation method. We describe the implementation of a trust management framework based on the proposed models in Section 5. Our implementation is based on a travel scenario consisting of taxi, hotel and airline services. This is followed by a review of related work in the literature in Section 6. The last section draws the conclusions and possible future extensions for the proposed models and the implementation. II. F UZZY T RUST DATA M ODEL The reputation of a Web Service is a reflection of its quality. The quality of a service is measured using a set of Quality of Web Service (QoWS) parameters such as performance, availability, reliability and response time. A service consumer, after consuming the service, can rate the service by giving feedback on the service’s QoWS parameters. Such service consumers are called raters and the provided feedback values are called ratings. Thus, the reputation of a service in our model represents a collective perception of the consumers that have interacted with or used it in the past. The perception of each consumer on a service it has invoked is called Personal Evaluation (PerEval). By nature, a consumer’s perception is subjective and imprecise. We propose a fuzzy reputation model to represent the consumers’ perception by adapting the model presented in [13]. The fuzzy representation of a service is based on the assumption that the ratings of a service can be expressed as a number in the range [0, 1]. This means a consumer’s personal evaluation of a service can be described by assigning values in the range [0,1] to QoWS parameters. Similarly, the overall personal evaluation of all consumers for a service can be expressed in the range [0,1] by normalizing the aggregate values. Thus, a service s is represented as a fuzzy set of QoWS parameters M (s), where the membership of a QoWS is given by aggregate value of personal evaluation of the service s by its past
consumers.
M (s) = {q, µs (q)|q ∈ QoW S}
(1)
where µs (q) : QoW S → [0, 1]. The relationships between a service s ∈ S and q ∈ QoW S is defined by a function F as follows.
F = S × QoW S → [0, 1]
(2)
where F (s, q) = µs (q). The membership function of a fuzzy set representing a service is a function of ratings. A service may have a large number of ratings recorded against it. It is important to note that not all raters provide ratings (PerEval) against all QoWS parameters. We define the membership function for a QoWS parameter q in a fuzzy set defining a service s as follows.
PR µs (q) = F (s, q) =
i=1 (P erEvali (q))
(3) R in which P erEvali (q) is the personal evaluation of a QoWS parameter q in rating i, and R is the total number of ratings recorded against q. With each service invocation, a service consumer may choose to provide a rating against a QoWS parameter. The provided rating will change the membership value of the parameter in the fuzzy set M (s). We explain the above defined model using an example as follows. Consider a hotel service (s) that have 100 raters (R = 100) in the community who are willing to share their past experience. Each rater has used the service only once and provided a rating against a QoWS parameter ”reliability” (q). Assume that the first 50 raters provided a personal evaluation 0.5 (P erEval(q)) and the remaining 50 raters 0.9 (P erEval(q)). The aggregate rating µs (q) for the QoWS parameter reliability is 0.7. This means that the QoWS parameter “reliability” is a member of a fuzzy set representing the hotel service M (s) with a degree of 0.7. This measures the satisfaction of past consumers with the service s in terms of QoWS parameters “reliability”. III. F UZZY T RUST Q UERY M ODEL We propose a trust service to realise the functionalities of the proposed fuzzy trust management framework. A service consumer invokes the trust service to get a reputation value of a service. Existing trust management frameworks support two types of trust queries. • Global Trust: The global trust refers to the collective perception of all past consumers of a service. Retrieve
top 10 hotel services is an example of a global trust query. Personalized Trust: The personalized trust refers to the perception of past consumers based on a consumer’s preferences. A consumer may specify preferences in different ways such as providing weights to the QoWS parameters or providing a threshold to the computed reputation values. In this paper, our focus is on the personalized trust where the consumer specifies the expected value of past consumers’ perception on QoWS parameters. Retrieve top 10 services with reliability > 0.9 is an example of a personalized trust.
the quality values associated with QoWS parameters. The set of fuzzy descriptors, denoted by T(Importance), for a term “importance” is defined as follows: T(Importance) = { important, very important, not important, not very important } . We then need to provide a semantic rule that provides the meaning to each linguistic descriptor in T (importance). We use compatibility functions to provide the semantics to the defined fuzzy descriptors. We define the compatibility function as a distance between the current membership value F (s, q) and the user specified value v.
A consumer can express a simple personalized query as “retrieve all s where q > v”, where q ∈ QoW S, s ∈ S and v the expected aggregate personal evaluation in the range [0, 1]. For example, a consumer who wants to find all available highly reliable “hotel” services can present a query as “retrieve all hotel where reliabiliy > 0.9”. The trust service returns a ranked list of hotel services with the value of QoWS parameter reliability > 0.9. The consumer uses v to personalise the trust value. The high value of v means the quality parameter is very important to the consumer, and the consumer expects the services to have high ratings from the past consumers. Therefore, v acts as a constraint on the QoWS parameter while evaluating the personalised reputation value for a service. Existing trust management frameworks support such personalized trust queries. However, the limitation of the above query is that a consumer needs to give a specific numerical quality value v with the query. Thus, the consumer must have a knowledge of the underlying data models. Instead of using quantitative values, it would be natural for a consumer to use qualitative values such as low, medium, large and high. It is possible to model such qualitative query terms by defining fuzzy concepts [14]. We next present a fuzzy linguistic query model for trust service, an adaptation of the model presented in [13]. The proposed fuzzy linguistic query model enables users to query services using linguistic descriptors for describing reputation information. Our trust service supports a more natural way of expressing the requirements than using quantitative quality values. For example, a user wants to retrieve a personalised ranked list of highly reliable hotel services. The user can use existing trust management frameworks and express their query as (reliability > 0.9). A more natural way of expressing this query would be using semantic terms. The same query can be expressed as (reliability is very important). This means the QoWS parameter reliability is very important for the user. This also means the parameter is expected to have high ratings from the past consumers of the services. We use linguistic descriptors such as important, very important, and not so important to provide semantics to
where i is a quality value delimiting the full satisfaction of constraint important, and D is the Euclidean distance. For example, the value of i is 0.6 for a query with linguistic term important. This means any services with an aggregate reliability value greater than 0.6 fully satisfies the query. In fuzzy theory, the ∪ can be represented as a max function. Using the equation (4), we can derive µimportant as:
•
M (q is important) = ∪v∈[i,1] D((F (s, q), i)
µimportant =
(4)
D(F (s, q) − i) 0 ≤ F (s, q) < i 1 i ≤ F (s, q) ≤ 1
Similarly, we now need to define the compatibility functions for hedges. In our linguistic descriptor set above, we have only one hedge, very. The hedge very is more restrictive. Thus, the compatibility function for very is defined as: very(µimportant (F (s, q))) = µimportant (F (s, q))2 The semantics of the operator not is defined as complement of the term important. not(µimportant (F (s, q)))) = 1 − µimportant (F (s, q)) We next explain the above model using an example of a hotel service H that has an aggregate personal evaluation for quality parameter “reliability” 0.2. We use the value of i = 0.6 for a query with linguistic term important. The evaluated values for H are as follows: µimportant (H) = 0.4, very(µimportant )(H) = 0.2, and not(µimportant )(H) = 0.6. This means a reputation query with a constraint “reliability is very important” to the trust service returns the service H with a reputation value 0.2. This also means the past consumers of the service H have rated its reliability 0.2 in the range from 0 to 1. IV. F UZZY T RUST Q UERIES We defined a fuzzy trust query model for a trust service in the last section. Based on the query model, we define fuzzy trust queries and their processing in this section. For
example, a user may want to retrieve trust information about “very reliable” services that “guarantee the preservation of privacy”. This query can be expressed using linguistic terms as: (reliability is very important) AND (privacy is very important) The atomic component for the fuzzy query is (q is x), where x is a linguistic term belonging to T (L), and q is a QoWS parameter belonging to QoW S. The set of atomic fuzzy trust query T Q is defined as QoW S × is × T (L). The set of all possible legitimate reputation-based trust query T Q∗ is defined by the following syntactic rules. 1) 2) 3) 4)
∀(q is x) ∈ T Q → (q is x) ∈ T Q∗ ∀Q1 , Q2 ∈ T Q → Q1 AN D Q2 ∈ T Q∗ ∀Q1 , Q2 ∈ T Q → Q1 OR Q2 ∈ T Q∗ ∀Q ∈ T Q∗ → N OT Q ∈ T Q∗
A. Trust Query Modes The trust service processes a user query and returns the results. A trust service must support different types of user queries. A user query may range from retrieving top k services based on the reputation information without being specific on QoWS parameters to as specific as retrieving top k services that are highly “reliable”. In the following, we define three modes of queries supported by the trust service. • Implicit mode: In the implicit mode, users do not specify any requirements on QoWS parameters. The user is trying to retrieve global trust value [15]. Therefore, it is implicitly assumed that all QoWS parameters are equally and very important. An example query in implicit mode is “retrieve top 10 most trusted taxi services”. This query can be translated to Qi = ∀q ∈ QoW S : (q is very important)
•
where all QoWS parameters are very important. It is essential for the trust service to support this type of query because some users may not be familiar with QoWS parameters and would not want to personalise the trust [15]. Furthermore, they may consider the overall quality based trust as more significant than a specific quality. Explicit mode: This mode assumes that all necessary information required are provided by the users in their queries. This type of query mode is better suited for the users who know exactly what they want. This is also referred to as personalized trust. For example, a user would like to retrieve the top 10 most trusted airline services based on low cost, high reliability and high privacy. This query can be translated to Qe =
(cost is important)AN D (reliability is very important)AN D (privacy is very important)
•
In the explicit mode, during trust evaluation, the trust service does not consider QoWS parameters that are not explicitly mentioned in the query. Hybrid mode: The hybrid mode is a combination of the above two modes. In this mode, users can explicitly mention their criteria for evaluating the trust value for a service using QoWS parameters. However, all QoWS parameters that are not mentioned in users queries take certain value implicitly. For example, a user would like to retrieve the top 10 most trusted hotel services based on low cost, high reliability and high privacy. In this query, the user explicitly mentioned three QoWS parameters. This query can be translated to Qh = ∀q ∈ U QoW S : Qe AN D ∀q ∈ (QoW S − U QoW S) : Qi where Qi , Qe and Qh are implicit, explicit and hybrid queries, respectively, and U QoW S ⊂ QoW S is a set of parameters specified by users in their query Qe . Depending on the query evaluation model, one can choose the linguistic value for evaluating Qi important or very important or not very important or not important.
B. Processing Trust Queries A fuzzy trust query Q may be atomic or complex. The processing of an atomic query is done by computing the compatibility function defined earlier. However, processing of the complex query is not straightforward as each atomic query is independent. The processing includes computing the compatibility of the quality of service parameter value F (s, q) with the linguistic term x for each stored service s ∈ S. The atomic query (q is x) is processed using an evaluation function Ea defined as follows. Ea = S × T Q → [0, 1]
(5)
This means, for a given fuzzy query (q is x), the function Ea measures how well the service s ∈ S is satisfying the given query. Similarly, we define an evaluation function Ec to process the complex queries. Ec = S × T Q∗ → [0, 1]
(6)
The evaluation can be done using a set of fuzzy functions [16]. We use the min-max rule to provide the semantics to the evaluation function Ec . Ec (s, Q) Ec (s, Q1 AN D Q2 ) Ec (s, Q1 OR Q2 ) Ec (s, N OT Q)
=
Ea (s, (q is x)) f or Q = (q is x) = min(Ec (s, Q1 ), Ec (s, Q2 )) = max(Ec (s, Q1 ), Ec (s, Q2 )) = 1 − Ec (s, Q)
The value returned by Ec is a reputation of a service for a given query, represented by a tuple < s, r >, where s is a service and r is its computed reputation value. The implicit and explicit queries are processed using the evaluation function Ec defined earlier. The results are then ranked based on the reputation value returned by Ec . However, the processing of the hybrid query is not straightforward as implicit and explicit components of the queries may need to be dealt with differently. We proposed a technique involving two steps as follows. First, we compute the explicit part of the query. The evaluation of explicit query component by using Ec (Qe ) returns a ranked list of services with a reputation value in the range [0,1]. The result is then grouped using returned reputation values into linguistic descriptors, as shown in the table below. Figure 1. T(Trustworthiness) very high fairly high medium low fairly low very low
lower value 0.8 0.6 0.4 0.3 0.2 0.0
higher value 1.0 0.8 0.6 0.4 0.3 0.2
Second, we rank the services in the same group by processing the implicit part of the query. All members in a group are considered to have the same fuzzy value for explicit query. Therefore, the services in each group are evaluated against the implicit query using Ec (Qi ) and then ranked according to the reputation value returned by the implicit query. V. P ROTOTYPE I MPLEMENTATION The proposed fuzzy trust management framework for Service Web has been implemented in the context of Web Service Management System (WSMS) [17]. The framework is implemented and deployed as a trust service. The trust service can be used independently as well as integrated as a component of WSMS. In the following, we discuss the implementation architecture, a travel scenario that has been implemented and the evaluation results for different types of queries in the travel scenario. A. Architecture Figure 1 shows the implementation architecture of the proposed fuzzy trust management framework. The architecture has following four major components. Community Manager: Our framework is based on the concept of a community. Services providing a similar type of service belong to the same community. The community
An implementation architecture
manager uses ontology to organize services into different types of community. For example, all airline services belong to an airline community. They also belong to the travel community if the concept airline is a sub-concept of a concept travel. In addition to organizing services into community, the community manager maintains raters and ratings. The raters are the consumers who are willing to share their experience with others, and the ratings are the feedback given by the consumers against a service’s QoWS parameters. The community manager is implemented and deployed as a Web Service. It provides appropriate interfaces to the other components of the trust service. Additionally, it provides Web portals for supporting a variety of functionalities such as rater registration, consumer feedback and service management (e.g., addition of new service or a new QoWS parameter for a service). Fuzzy Query Interface: This component provides a query interface to the users of the trust service. The current implementation supports two types of interfaces: Web service and Web portal. The Web service interface is used by other Web services to query reputation of a service (or services). For example, a service orchestrator in WSMS may use the Web service interface to invoke the trust service to inquire about the reputation of a particular service. The Web portal interface is designed to be used by an end user (or customer). This allows the trust service to be invoked independently and directly by the customer. Fuzzifier: The main purpose of this component is to convert raw ratings into fuzzy ratings. It takes raw ratings and compatibility functions as inputs and provides the fuzzy ratings as outputs. The fuzzy query evaluator calls
this component when a fuzzy rating is needed. For query optimization, the trust service uses the fuzzifier to calculate and store the fuzzy ratings in a cache. Fuzzy Query Evaluator: This is the core component of the trust service. It takes the fuzzy query from the interface and performs a syntax check against the context free grammar defined in the Section 4. The valid query is then evaluated using the proposed query evaluation algorithms. The results of the evaluation of different types of query processing algorithms will be presented later in this section. The query evaluator uses fuzzy ratings from cache if available. Otherwise, it provides appropriate compatibility functions and invokes the fuzzifier to get fuzzy ratings. Once the query is evaluated, it prepares a ranked list of results and returns it to the requester via the query interface. B. Travel Scenario Our prototype implementation considers a travel scenario with three communities: airline, taxi and hotel. We use an ontology to define these communities. The airline community has five services described using five QoWS parameters. Similarly, there are five services in the the taxi community and ten services in the hotel community. Each of these services are described using four QoWS parameters. We implemented our architecture for the travel scenario. Our current implementation has two Web services and a number of Web portals for invoking those services. The Web services and portals were developed using Java and deployed on Apache Tomcat/5.5.27. The services were deployed using the Apache Axis2 Web service version 1.4.1. The Web services and their access stubs were created using the axis2 eclipse plugins, Axis2 Service Archiver and Axis2 Code Generator. C. Evaluation Results This section presents an evaluation of different types of query processing algorithms. Figure 1 shows that the proposed fuzzy trust management framework is implemented on top of current feedback-based reputation systems. The purpose of the evaluation is to make a comparative study of “global trust query” on feedback-based reputation systems with the ”personalized trust query” on the proposed trust service (see Section 3). We consider four types of queries: top k, implicit, explicit and hybrid. The top k queries are global trust queries and evaluated on raw ratings collected from the feedback systems, whereas the other three types of queries are evaluated on fuzzy ratings. The evaluation system has 1001 invocations for each of the three communities (taxi, hotel and airline), i.e., there are total 3003 invocations and around 13000 ratings when we consider all ratings against all QoWS parameters. The evaluation is performed on a system with CPU Pentium D 3.00 GHz and RAM 0.99 GB.
Table I P ERCENTAGE IMPROVEMENT OF CACHE OVER NON - CACHE Comm / algs airline taxi hotel
top k 85 85 79
implicit 90 90 92
explicit 91 90 92
hybrid 90 90 92
We consider a query “retrieve top 5 services” from the given community. First, the query is performed on a system without cache. The results for the evaluation is shown in Figure 2. As can be observed from the results, the number of services has a direct impact on the performance of top-k queries. The hotel community with 10 services needed about 37% more time than the airline and taxi communities with 5 services as shown in Figure 2 (a). This is expected as the method evaluates the global reputation of all the services in the community to find the top k services. Similarly, we also observed that the number of QoWS parameters in a query has a direct influence on the performance of fuzzy queries. The higher the number of QoWS parameters, more time required to process the queries as shown in Figure 2(c). We evaluate the fuzzy queries as follows. For the fuzzy explicit query, we split the query into simple queries and evaluate how well each service in the community meets the query. This list of service value pairs is then sorted in descending order and the top k services from the list is returned. In the implicit query, a query is constructed using all QoWS parameters from the services in the community. It thus forms a query as a conjunction of simple queries on each QoWS parameter. All services are assessed for each query and the minimum value is taken as a final value. The service list is then sorted and the top k services returned. In the hybrid mode, we first perform explicit query evaluation based on the specified QoWS parameters and sort the results. We group the results and then perform an implicit query with the QoWS parameters not used in the explicit query. The results obtained are sorted within each group and the top k services are returned. As can be seen in the 2, the processing times for these queries are relatively high. To improve the processing times, we use a cache. The results of using a cache are shown in Figure 2 (b) and (d). It shows that we can achieve about 80 to 90 percent improvement on the performance using a cache over a non-cache system as shown in Table I. VI. R ELATED W ORK Trust management has been studied in a variety of disciplines including economics, computer science, marketing, politics, sociology and psychology [1], [7], [5], [18], [19], [15]. In computer science, trust management is effectively used in application domains such as e-business, peer-topeer (P2P) networks, grid computing systems, multi-agent systems, Web search engines and ad-hoc networks [3], [20],
Figure 2.
Evaluation results for different number of services in non-cache and cache cases
[4]. These trust management techniques have been adapted and used in Service Web [21], [9]. O’Hara and Hall [22] present some research challenges for trust management on the Web and discuss issues such as provenance and social networks in the semantic Web. In the following, we give a brief overview of the trust management techniques that are closely related to our work. Reputation-based trust management systems have gained a lot of attention due to their success in e-commerce systems [8]. Amazon, eBay and Yahoo! Auctions are examples of businesses that have deployed reputation systems successfully. These reputation systems use feedback from the consumers as a reputation measure [11]. However, there are many known drawbacks of these feedback based reputation systems such as retaliation, reciprocation and dishonest or biased users [10]. Majority of these issues relate to capturing user feedback. User feedback is subjective and imprecise in nature. Therefore, it is important to consider this while evaluating the reputation of a service. To address this issue, this paper proposed a fuzzy reputation-based trust management system. Next, we review the fuzzy trust management systems
that are closely related to our work. Manchala [23], [24] proposed trust matrices based model and protocol for e-commerce transactions. Fuzzy matrices are defined on the transaction history to establish transaction trust. The use of fuzzy concepts is limited to transactions and not directly applicable to reputation systems for Service Web. This fuzzy approach could be adapted to feedback on QoWS parameters, but it requires the approach to be redesigned and redeveloped. Furthermore, the paper presents only the fuzzy model without defining formal underlying data and query models. Another work that is closely related to the proposed framework is REGRET [25], [26], [27]. It uses fuzzy rules to define different types of relationships among agents in the context of social networks. In REGRET, the use of fuzzy concepts is very limited and underlying data, query models and evaluation algorithms have not been defined as in our framework. Furthermore, REGRET is designed to be used in agent systems and our framework for the Service Web. Therefore, our proposed architecture and algorithms are different to that of REGRET, and uniquely designed for Service Web.
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