The Semantic Web is gaining immense popularity-and with it, the Resource Description Framework (RDF)broadly used to model Semantic Web content. However, access control on RDF stores used for single machines has been seldom discussed in the literature. One significant obstacle to using RDF stores defined for single machines is their scalability. Cloud computers, on the other hand, have proven useful for storing large RDF stores, but these system slack access control on RDF data to our knowledge. This work proposes a token-based access control system that is being implemented in Hadoop (an open source cloud computing framework). It defines six types of access levels and an enforcement strategy for the resulting access control policies. The enforcement strategy is implemented at three levels: Query Rewriting, Embedded Enforcement, and Post processing Enforcement. In Embedded Enforcement, policies are enforced during data selection using MapReduce, whereas in Post-processing Enforcement they are enforced during the presentation of data to users. Experiments show that Embedded Enforcement consistently outperforms Post processing Enforcement due to the reduced number of jobs required.