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Three use cases will be conducted to validate the proposed approach.
Ciphertext Policy Attribute-based Encryption (CP-ABE) is recognized as one of the most effective approaches for data access control solution in cloud computing.
Therefore a proper risk assessment methodology is required to determine the asset-specific and stakeholder-specific risks so as to be able to control them.
Existing methodologies fail to comprehensively evaluate various risk elements like asset value, vulnerabilities and threats.
However, the dynamicity inherent in cloud environments, coupled with the heterogeneous nature of cloud services, hinders the formulation of effective and interoperable access control policies that are suitable for the underlying domain of application.
To this end, this work proposes an ontological template for the semantic representation of context expressions in access control policies.
This is because it provides efficient key management based on user attributes of multiple users in accessing shared data.
However, one of the major drawbacks of CP-ABE is the privacy of policy content.
However, this kind of resource-sharing environment may face the emergence of free riders, which only consume resources without caring for the whole, in a modern version of the Tragedy of the Commons.
Assets of Cloud stakeholders (Service Providers, Consumers and Third Parties) are the essential elements required to carry out necessary functions / services of the cloud system.
Assets usually contain vulnerabilities that may be exploited by threats to jeopardize the functioning of the cloud system.
On the other hand, those with low expected returns to the system, free riders for example, face disadvantages or even are eliminated from the tournament.
We show preliminary tests of a score function within the tournament, illustrating how the system promotes or eliminates participants according to their behavior.
Then, given a set of connections that map outputs of some Spark applications to free inputs of other Spark applications, we automatically embed Spark applications with the required synchronization and communication to properly run them according to the user-defined mapping.