The exponential data growth in intelligent environments fuelled by the Internet of Things is not only a major push behind distributed programming frameworks for big data, it also magnifies security and privacy concerns about unauthorized access to data. The huge diversity and the streaming nature of data raises the demand for new enabling technologies for scalable access control that can deal with the growing velocity, volume and variety of volatile data.

This paper presents SparkXS, an attribute-based access control solution with the ability to define access control policies on streaming latent data, i.e. hidden information made explicit through data analytics, such as aggregation, transformation and filtering. Experimental results show that SparkXS can enforce access control in a horizontally scalable way with minimal performance overheads.