Trust assessment at runtime

The main functionality provided by the decision/reason engine is the trust assessment. There are several existing mechanisms that can be applied for assessing trust through evidence. Here subjective logic (SL) [Jos01] has been chosen as the formal base for trust assessment because of its sound mathematical foundation in dealing with evidential beliefs; and the inherent ability to express uncertainty explicitly. The SL consists of a belief model called opinion and a set of operations for aggregating opinions. Herein, we apply a simplified scheme of the SL as in [JoK98, LVW04, Twi03].

5.7.1 Notations and Definitions

We develop and use several new SL operators to illustrate how to assess trust based on the formal trust model in the trust management framework.

Notation 1:

1. An opinion ω=(b,d,u)∈Ω, where b+d+u=1, and ^b,d,u∈

[ ]

^0,1. b is the belief, d is the disbelief, u is the uncertainty of the opinion and Ω is the set of all opinions. In particular, _ωi is the opinion on the quality attribute qa_i, and

ωA is an opinion about a proposition A;

2. QA is the set of quality attributes qa that may influence an entity’s opinion importance rates of the attributes. It also makes it easy to consider other influencing factors of trust through weighting, such as time element, similarity of recommendations to trustor’s own experience and similarity of different contexts, etc. It is also flexible to support applying other forms of trust value expression.

The weighted summation operator can be applied to a finite set of m opinions times. The combination is also based on weights.

Notably, the operator ^∑ sits beyond the original theory of the SL. It supports a special case that all _ωi hold the same base rate a (the default value is 0.5) [Jos01].

In addition, based on experimental study, it can provide similar results to the results through applying the SL original operators ₁

{

ⁱ

(

^{i,1 i,0}

) }

together [TLU06]. Thereby, the operator ^∑ provides a shortcut for the aggregation of trust values expressed as SL opinions.

Definition 2.

Let ^ω=

(

^b,d,u

)

^.

Then _θ:(p,n,r)→_ω is the opinion generator, such that b+d+u=1,

(

^{p n r}

)

p

b= / + + , ^d=n/

(

^p+n+r

)

^{, and u= /r}

(

^p+n+r

)

^.

Particularly, p is the positive points of evidence on a proposition, n is the negative points of evidence on the proposition, r≥1 is a parameter controlling the rate of loss of uncertainty, which can be used to tune the use of uncertainty in the model for the requirements of different scenarios (we often take r=2).

The operator _θ is used to generate an opinion based on positive and negative evidence [JoK98]. Note that other definitions on calculating b, d and u can also be applied. opinions [LVW04]. Note that _≥op is a partial order operator.

5.7.2 Trust Assessment Algorithm

At runtime, the quality attribute monitor monitors the trustee’s performance with respect to its quality attributes. The monitoring is based on or driven by the criteria or policies set by the trustor entity who registers itself firstly at the trust management framework in order to manage trust of its specified trustee.Monitoring based method for trust (re-)evaluation was defined in the concept of trust management by Grandison and Sloman [GrS00]. A runtime observation based method through applying a security wrapper to simulate a component contract situation for collecting useful information for trust evaluation on the software component was proposed in [Her01]. In the experience base, for each quality attribute, if the monitored performance is better than the criteria (saved in the policy base), the positive points of that attribute are increased by 1. If the monitored result is worse than the criteria, the negative points of that attribute are increased by 1.

The opinion of each quality attribute can be generated based on the opinion generator _θ [JoK98]. In addition, based on the importance rates of different quality attributes, a combined opinion on the trustee can be calculated by applying the weighted summation operator. By comparing to the trust threshold opinion (to), the decision engine can decide if the trustee is still trusted or not. The algorithm for trust assessment at runtime is described below.

Initialization

te: the assessed target (a system or subsystem or a service) py(to, ir, ep, cr): the policy on te:

1. Monitor te’s performance regarding te’s quality attributes in

4. Based on the importance rates on different attributes, calculate the combined opinion: ^=∑

{ }

which can be specified by the trustor. This algorithm has been tested, implemented and integrated into the EU ITEA Trust4All platform as one of the mechanisms for trust assessment on the platform entities. The Trust4All platform is a Linux based middleware platform for component software.

There are a number of operators provided by the SL to conduct operations on the SL opinions. Herein, we use the newly defined weighted summation operator.

There are several reasons. Firstly, we can not find any existing SL operator that can directly support aggregating a number of opinions on one entity’s quality attributes together. Secondly, as specified above, the weighted summation operator is a shortcut operation of applying the SL original operators. That is using the Discounting operator ⊗ to weight _ωi with discounting opinion (w_i,1−w_i,0), and then applying Bayesian Consensus operator ⊕ to aggregate all discounted _ωi together [TLU06]. Thirdly, using the weighted summation operator could make the trust assessment autonomic. The weights or important rates can be automatically generated based on the high level policies of the trustor, for example, time distance or the deviation of context similarity and opinion similarity. Finally, using the weighted summation operator is compatible with the trust control model as will be introduced in Section 5.8. Thereby, the trust assessment at runtime introduced herein can cooperate with the trust control mode prediction and selection as well as adaptive trust control model adjustment in order to provide an autonomic trust management solution.

5.7.3 General Criteria Support

In the proposed formal trust model, we support the criteria for setting positive points p_{qa_i} or negative points n_{qa_i} on different quality attributes. Particularly, it is also possible to support general criteria that specify the conditions of trust related to several quality attributes. The trust opinions calculated based on the general criteria can also be aggregated based on the importance rates.

In addition, a tracing factor (tf) can be introduced to support a special case that when the opinion on a trust influencing factor (e.g. a quality attribute) is below some threshold, trust will be totally lost. We set tf =0 if it happens. The final trust

value will be tailored as ^{ω =f ωf}

(

^bf,df,uf

)

^{, where bf =b∑∗tf =0, df =b∑+d∑,}

= uΣ

u_f . If trust is partially lost in this case, 0< tf <1, and b_f =b∑∗tf ,

∑

∑ − +

=b tf d

d_f (1 ) , u_{f = uΣ}.

In document Trust management for mobile computing platforms (Page 71-75)