| Summary: | The expansion of the smart devices, the growing popularity of the social networks, and the wide spread of the corporate services impose huge amounts of heterogeneous data to be generated and stored in separate silos on a daily basis. Parts of this data are private and highly sensitive as they reflect owner's behavior, obligations, habits, and preferences. On the other hand, the emerging crowd services challenge the owners to expose these data in return to the convenience they offer. Therefore, it is imperative not only to protect the interaction with sensitive data, but also to selectively open it in an unharmful manner for the owner's personal integrity. One of the main enablers of the crowd services is the emerging linked data, which is all about opening heterogeneous knowledge from separate data silos. Its growing popularity encourages the data owners to publish their personal data in linked data format. The fusion of sensor, social, and corporate data opens new security challenges, which extend the standard security considerations toward more flexible and context aware authorization platforms. In this paper, we propose a linked data authorization (LDA) platform atop a policy language flexible enough to cover all newly emerged requirements, including context awareness. The proposed policy language extends the widely accepted W3C's SPARQL query language and leverages its expressiveness to protect every part of the data. The novelty of our LDA platform is its unique capability of design time policy validation through stand-alone testing, conflict detection, and overall protection coverage extraction.
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