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It is a Boolean function, and may range over the subject, resource and action of the request as well as arbitrary environment variables. If a request matches a rule target the condition of the rule is evaluated. Evaluating the condition will involve consulting the environment of the system. If the condition evaluates to true the effect of the rule will be returned (which may be either permit or deny). If the condition evaluates to false the rule will return not applicable. If the condition cannot be evaluated (perhaps because some system-level environmental information is missing) the rule will return indeterminate.

Q ≡ (∀ : canon • (∀ ob • P ob ⇒ Q ob)) DFD refinement allows the definition of correctness in terms of any set of modalities. The next Venn style diagram shows the corresponding sets of observations for SDs ‘P ’ and ‘Q ’ with respect to two modalities - called ‘grey’ and ‘dash’. Q P ✲ ✛ ✲ P ✛ Q The role of the canon in DFD refinement is demonstrated with a short list of refinement statements consistent with the above modalities and SDs. P {grey}. Q P {dash}. Q P {grey, dash}. Q Q . P Including discordance as a canonic modality makes refinement a subset of fixing.

Like policies, rules in XACML may contain a target. They will also contain an effect (permit or deny). Optionally, they may also contain a condition. A condition is the part of a policy which is context-sensitive. It is a Boolean function, and may range over the subject, resource and action of the request as well as arbitrary environment variables. If a request matches a rule target the condition of the rule is evaluated. Evaluating the condition will involve consulting the environment of the system.

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Fuelless Gravity Engine


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